Facial Emotion Detection¶
Executive Summary¶
- Project Aim: We developed a neural network model capable of identifying human emotions from facial expressions captured in images.
- Data Utilization: Our model training utilized a dataset comprised of grayscale images, each labeled with one of four distinct emotions: happy, neutral, sad, or surprise.
- Model Exploration: We engaged in extensive testing of various neural network architectures, including both custom-designed Convolutional Neural Networks (CNNs) and established transfer learning frameworks such as VGG16, ResNet50V2, and EfficientNetV2B0.
- Performance Metrics: The models were rigorously evaluated on multiple metrics including accuracy, precision, recall, and F1-score, aided by detailed confusion matrices for in-depth analysis.
- Best Performer: The standout model was a complex CNN featuring five convolutional blocks, which yielded the highest accuracy and performance metrics in tests.
- Ethical Considerations: For future deployments of the emotion detection model, we raise concerns about ethical considerations, focusing on ensuring privacy and fairness in real-world applications.
- Real-World Application: This model holds potential for significant impact in various sectors by enhancing user interface experiences and providing support tools for mental health professionals.
- Future Work: We propose further refinement of the model and the integration of additional safeguards to address ethical concerns more robustly.
Context¶
Deep learning has been increasingly applied to tasks involving less structured data types like images, texts, audio, and video in recent years. These endeavors often aim to achieve human-like proficiency in processing such data, leveraging our innate ability to intelligently interact with complex, unstructured information. Within the realm of AI, a field known as Artificial Emotional Intelligence, or Emotion AI, focuses on creating technologies that can understand human emotions by analyzing body language, facial expressions, and voice tones, and respond to them effectively.
Recognizing facial expressions plays a vital role in human-computer interaction. Research indicates that facial expressions and other visual signals account for about 55% of how we convey emotions. Thus, developing a model capable of accurately recognizing facial emotions is a significant stride toward equipping machines with AI that exhibits emotionally intelligent behavior. Systems that can automatically recognize facial expressions have a broad range of potential applications, from understanding human behavior to diagnosing psychological conditions, and improving the interaction quality of virtual assistants in customer service settings.
Objective¶
The goal of this project is to use Deep Learning and Artificial Intelligence techniques to create a computer vision model that can accurately detect facial emotions. The model should be able to perform multi-class classification on images of facial expressions, to classify the expressions according to the associated emotion.
Key Questions¶
Throughout the project, we will be answering the following questions:
- How accurately can the deep learning model identify and classify different facial emotions (happy, sad, surprise, neutral) from images?
- How well does the model generalize to new, unseen images? Can it maintain high accuracy across the test, train, and validation datasets?
- How does the different model architectures compare in terms of accuracy to classify the different emotions?
- What are the potential applications of the developed model, and what implications might its deployment have in some industry fields?
Problem Formulation¶
We are tasked with leveraging Deep Learning techniques to develop a computer vision model capable of accurately detecting and classifying facial emotions. The model needs to distinguish between four specific emotions (happy, sad, surprise, neutral) based on images of facial expressions. This task involves multi-class classification, requiring the model to predict the correct category of emotion for each image it processes.
About the dataset¶
The data set consists of 3 folders, i.e., 'test', 'train', and 'validation'. Each of these folders has four subfolders:
‘happy’: Images of people who have happy facial expressions.
‘sad’: Images of people with sad or upset facial expressions.
‘surprise’: Images of people who have shocked or surprised facial expressions.
‘neutral’: Images of people showing no prominent emotion in their facial expression at all.
Importing the Libraries¶
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import zipfile
import random
from PIL import Image
from typing import List
from datetime import datetime
# For Data Visualization
import seaborn as sns
# For Model Building
import tensorflow as tf
import keras
from tensorflow.keras.models import Sequential, Model # Sequential API for sequential model
from tensorflow.keras.layers import Dense, Dropout, Flatten, Input # Importing different layers
from tensorflow.keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization, LeakyReLU, ReLU
from tensorflow.keras import backend
from tensorflow.keras.optimizers import Adam, RMSprop # Optimizers for optimizing the model
from tensorflow.keras.callbacks import EarlyStopping # Regularization method to prevent the overfitting
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.preprocessing.image import load_img, ImageDataGenerator
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from keras.applications.vgg16 import preprocess_input as preprocess_input_vgg16
from keras.applications import VGG16
from keras.applications.resnet_v2 import preprocess_input as preprocess_input_resnetv2
from keras.applications import ResNet50V2
from keras.applications.efficientnet_v2 import preprocess_input as preprocess_input_efficientnetv2
from keras.applications import EfficientNetV2B0
2024-04-11 00:04:06.669124: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-11 00:04:07.217980: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Let us load and unzip the data¶
Note:
- You must download the dataset from the link provided on Olympus and upload the same on your Google drive before executing the code in the next cell.
- In case of any error, please make sure that the path of the file is correct as the path may be different for you.
# Storing the path of the data file from the Google drive
path = "/home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/Facial_emotion_images.zip"
# The data is provided as a zip file so we need to extract the files from the zip file
with zipfile.ZipFile(path, "r") as zip_ref:
zip_ref.extractall()
Preparing the Data¶
The dataset has three folders, i.e., 'train', 'validation' and 'test'. Each of these folders has four sub-folders, namely 'happy', 'neutral', 'sad', and 'surprise'.
We will have the train, validation and test path stored in a variable named 'SUBDIRS', and a base directory 'DATADIR'.
The names of the sub-folders, which will be the classes for our classification task will be stored in an array called 'CATEGORIES'.
DATADIR = "/home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/Facial_emotion_images" # Base directory
SUBDIRS = ["train", "validation", "test"] # Subdirectories
CATEGORIES = ["happy", "neutral", "sad", "surprise"] # Emotion categories
We are going to check the size of one image, and then check if all the other images have the same size. In case, they are different, we'll resize the ones that are different.
def get_first_image_size(directory, sub_dirs, categories):
"""
Returns the size of the first image found in the specified directories.
Parameters:
- directory (str): The base directory of the dataset.
- sub_dirs (list of str): Subdirectories to search through (e.g., ['train', 'validation', 'test']).
- categories (list of str): Categories (e.g., ['happy', 'neutral', 'sad', 'surprise']).
Returns:
- tuple: Size of the first image found (width, height).
"""
for sub_dir in sub_dirs:
for category in categories:
path = os.path.join(directory, sub_dir, category)
for img_name in os.listdir(path):
img_path = os.path.join(path, img_name)
with Image.open(img_path) as img:
return img.size # Return the size of the first image found
# Get the size of the first image
expected_size = get_first_image_size(DATADIR, SUBDIRS, CATEGORIES)
print(f"Expected size of the first image: {expected_size}")
Expected size of the first image: (48, 48)
def check_image_sizes(directory, sub_dirs, categories, target_size):
"""
Checks if all images in the specified directories match the target size.
Parameters:
- directory (str): The base directory of the dataset.
- sub_dirs (list of str): Subdirectories to search through.
- categories (list of str): Categories to search through.
- target_size (tuple): The expected size of the images (width, height).
Returns:
- bool: True if all images match the target size, False otherwise.
"""
all_match = True # Flag to keep track of size match
for sub_dir in sub_dirs:
for category in categories:
path = os.path.join(directory, sub_dir, category)
for img_name in os.listdir(path):
img_path = os.path.join(path, img_name)
with Image.open(img_path) as img:
if img.size != target_size:
print(f"Image {img_path} has a different size: {img.size}, expected: {target_size}")
all_match = False
return all_match # Return early upon first mismatch
return all_match
# Check if all images match the expected size
all_match = check_image_sizes(DATADIR, SUBDIRS, CATEGORIES, expected_size)
if all_match:
print("All images match the expected size.")
else:
print("Not all images match the expected size.")
All images match the expected size.
Visualizing our Classes¶
Let's look at our classes.
Write down your observation for each class. What do you think can be a unique feature of each emotion, that separates it from the remaining classes?
def visualize_emotion_images(directory: str, sub_dirs: List[str], emotion: str, image_count: int = 9) -> None:
"""
Visualizes a specified number of images from a given emotion class directory across specified subdirectories.
Parameters:
- directory (str): The base directory where emotion class folders are located across subdirectories.
- sub_dirs (List[str]): List of subdirectories ('train', 'validation', 'test') to search through.
- emotion (str): The specific emotion class to visualize images from.
- image_count (int): The number of images to display. Defaults to 9.
Returns:
- None: This function does not return any value but displays images inline.
"""
image_paths: List[str] = [] # To store paths of images to be displayed
# Iterate through the specified subdirectories to collect image paths
for sub_dir in sub_dirs:
emotion_dir: str = os.path.join(directory, sub_dir, emotion)
if os.path.isdir(emotion_dir):
for img_name in os.listdir(emotion_dir):
img_path = os.path.join(emotion_dir, img_name)
image_paths.append(img_path)
# If there are no images found for the emotion, print a message and return
if not image_paths:
print(f"No images found for the specified emotion: {emotion}")
return
# Select a random subset of image paths
selected_image_paths: np.ndarray = np.random.choice(image_paths, min(image_count, len(image_paths)), replace=False)
# Setup for plotting
fig = plt.figure(figsize=(4, 4))
columns: int = 3
rows: int = image_count // columns + (1 if image_count % columns else 0)
# Iterate over the selected images and display them
for i, image_path in enumerate(selected_image_paths, start=1):
ax = fig.add_subplot(rows, columns, i)
image = load_img(image_path, target_size=(48, 48)) # Ensure the image is resized to 48x48
plt.imshow(image)
plt.axis("off")
plt.tight_layout()
plt.show()
Happy¶
visualize_emotion_images(DATADIR, SUBDIRS, "happy", 9)
Observations and Insights:
The images appear to be in grayscale and vary in terms of lighting, contrast, and clarity.
The images display a range of happy expressions, from broad smiles showing teeth to subtle smiles without teeth. Also a diversity of subjects in terms of age, gender and also ethnicity.
Sad¶
visualize_emotion_images(DATADIR, SUBDIRS, "sad", 9)
Observations and Insights:
- The images capture a wide spectrum of sadness, from subtle, somber expressions to more overt manifestations like crying.
- The dataset includes faces with different orientations and features. Some faces are directly looking at the camera, while others are tilted or partially turned away.
Neutral¶
visualize_emotion_images(DATADIR, SUBDIRS, "neutral", 9)
Observations and Insights:
- The defining characteristic of these images is the absence of clear, expressive features that denote a specific emotion.
- Some faces may have subtle features that could be misconstrued as expressing a mild emotion.
Surprised¶
visualize_emotion_images(DATADIR, SUBDIRS, "surprise", 9)
Observations and Insights:
- The images showcase a range of intensities of surprise, from wide-eyed and open-mouthed expressions to more subdued, raised-eyebrow looks.
- The subjects vary in age, including both infants and adults.
Checking Distribution of Classes¶
# Function to count images in each category
def count_images(data_dir, categories):
counts = []
for category in categories:
path = os.path.join(data_dir, category)
count = len([name for name in os.listdir(path) if os.path.isfile(os.path.join(path, name))])
counts.append(count)
return counts
SUBDIRS_DICT = {"train": "train", "validation": "validation", "test": "test"}
# Counting images in each dataset
train_counts = count_images(os.path.join(DATADIR, SUBDIRS_DICT["train"]), CATEGORIES)
validation_counts = count_images(os.path.join(DATADIR, SUBDIRS_DICT["validation"]), CATEGORIES)
test_counts = count_images(os.path.join(DATADIR, SUBDIRS_DICT["test"]), CATEGORIES)
# Create DataFrames and format for easier reading
def create_df(counts, categories, dataset_name):
df = pd.DataFrame({"Class": categories, "Count": counts})
df["Percentage"] = (df["Count"] / df["Count"].sum()) * 100
df.set_index("Class", inplace=True)
# Formatting for easier reading
df["Count"] = df["Count"].apply(lambda x: f"{x:,}") # Adds commas to thousands
df["Percentage"] = df["Percentage"].apply(lambda x: f"{x:.2f}") # Rounds to two decimals
print(f"{dataset_name} Data Distribution:")
print(df)
total_images = df["Count"].str.replace(",", "").astype(int).sum()
print(f"Total images in {dataset_name}: {total_images:,}\n") # Formats total count with commas
create_df(train_counts, CATEGORIES, "Training")
create_df(validation_counts, CATEGORIES, "Validation")
create_df(test_counts, CATEGORIES, "Testing")
Training Data Distribution:
Count Percentage
Class
happy 3,976 26.32
neutral 3,978 26.33
sad 3,982 26.36
surprise 3,173 21.00
Total images in Training: 15,109
Validation Data Distribution:
Count Percentage
Class
happy 1,825 36.67
neutral 1,216 24.43
sad 1,139 22.89
surprise 797 16.01
Total images in Validation: 4,977
Testing Data Distribution:
Count Percentage
Class
happy 32 25.00
neutral 32 25.00
sad 32 25.00
surprise 32 25.00
Total images in Testing: 128
Think About It:
- Are the classes equally distributed? If not, do you think the imbalance is too high? Will it be a problem as we progress?
- Are there any Exploratory Data Analysis tasks that we can do here? Would they provide any meaningful insights?
Observations and Insights:
- Training Data: The training dataset shows a relatively balanced distribution among the classes of 'happy', 'neutral', and 'sad', each comprising approximately 26% of the dataset. However, 'surprise' is slightly underrepresented, making up 21% of the data. We'll see on the results if this is noticeable.
- Validation Data: In the validation dataset, there's a more pronounced imbalance. 'Happy' expressions dominate at 36.67%, followed by 'neutral' at 24.43%, 'sad' at 22.89%, and 'surprise' at 16.01%. This distribution deviates more significantly from an even split, indicating a potential bias towards 'happy' expressions.
- Testing Data: The testing dataset is perfectly balanced, with each class representing 25% of the data. This uniform distribution is ideal for evaluating the model's performance across all classes evenly.
Creating our Data Loaders¶
In this section, we are creating data loaders that we will use as inputs to our Neural Network.
You have two options for the color_mode. You can set it to color_mode = 'rgb' or color_mode = 'grayscale'. You will need to try out both and see for yourself which one gives better performance.
Tested data: We have tested both 'grayscale' and 'rgb', and we got better results with 'grayscale', which makes sense as the images are in grayscale.
# Set this to 'grayscale' as the images are in grayscale
color_mode = "grayscale"
# As we have checked, all images are 48x48, we will set the img_width and img_height to 48
img_width, img_height = 48, 48
color_layers = 1
# A batch size of 32 is appropriate for this dataset provide to provide a good balance
# between the model's ability to generalize (avoid overfitting) and computational efficiency.
batch_size = 32
# Training Data Augmentation
train_datagen = ImageDataGenerator(
rescale=1.0 / 255, # Normalize pixel values to [0,1]
horizontal_flip=True, # Faces are symmetric; flipping can simulate looking from another direction
brightness_range=(0.5, 1.5), # Randomly adjust brightness to simulate different lighting conditions
shear_range=0.3, # Shear transformations for perspective changes
rotation_range=20, # Slight rotation to introduce variability without distorting emotion features
width_shift_range=0.1, # Slight horizontal shifts to simulate off-center faces
height_shift_range=0.1, # Slight vertical shifts to account for different heights/angles
zoom_range=0.1, # Small zoom in/out to simulate closer or further away faces
)
# Validation and Testing Data should not be augmented!
validation_datagen = ImageDataGenerator(rescale=1.0 / 255)
test_datagen = ImageDataGenerator(rescale=1.0 / 255)
# Creating train_dir, validation_dir, and test_dir directories using the structure of DATADIR and SUBDIRS
train_dir = os.path.join(DATADIR, SUBDIRS_DICT["train"])
validation_dir = os.path.join(DATADIR, SUBDIRS_DICT["validation"])
test_dir = os.path.join(DATADIR, SUBDIRS_DICT["test"])
# Train Generator
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode, # Set to 'grayscale'
class_mode="categorical",
)
# Validation Generator
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode, # Set to 'grayscale'
class_mode="categorical",
shuffle=False, # shuffle=False to keep data in order for evaluation
)
# Testing Generator
test_generator = test_datagen.flow_from_directory(
test_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode, # Set to 'grayscale'
class_mode="categorical",
shuffle=False, # shuffle=False to keep data in order for testing
)
Found 15109 images belonging to 4 classes.
Found 4977 images belonging to 4 classes.
Found 128 images belonging to 4 classes.
Let's look at some examples of a batch of augmented training data.
# Fetch a batch of images and labels
images, labels = next(train_generator)
# Assuming the labels are one-hot encoded, we need to convert them back to class indices
labels_indices = labels.argmax(axis=1)
# Mapping of indices to class names, based on the 'class_indices' attribute of the generator
index_to_class = {v: k for k, v in train_generator.class_indices.items()}
fig, axes = plt.subplots(4, 4, figsize=(8, 8))
for image, label_index, ax in zip(images, labels_indices, axes.flatten()):
ax.imshow(image.squeeze(), cmap="gray") # Squeeze and cmap for grayscale
class_name = index_to_class[label_index]
ax.set_title(class_name)
ax.axis("off")
plt.tight_layout()
plt.show()
Model Building¶
Think About It:
- Are Convolutional Neural Networks the right approach? Should we have gone with Artificial Neural Networks instead?
Answer: Convolutional Neural Networks (CNNs) are the right approach for facial emotion classification on images, as they excel at capturing spatial hierarchies and patterns in visual data, which is critical for this type of task, unlike traditional Artificial Neural Networks (ANNs) which do not inherently process spatial information.
What are the advantages of CNNs over ANNs and are they applicable here?
Answer: CNNs have the advantage of being able to automatically and efficiently learn spatial hierarchies of features from images (thanks to their convolutional layers and shared weights), making them particularly suitable for image-based tasks like facial emotion classification, where recognizing spatial relationships and patterns within the images is key to accurate classification.
Creating the Base Neural Network¶
Model 1 Architecture:¶
- This is the first CNN Model, designed with a sequential architecture comprising three convolutional blocks, each followed by max-pooling and dropout layers for feature extraction and regularization.
- The first convolutional block starts with a Conv2D layer having 64 filters and a 3x3 kernel size, utilizing 'relu' activation and 'same' padding to maintain the input size, paired with a MaxPooling2D layer with a 2x2 pool size and 'same' padding, and a Dropout layer with a rate of 0.2 to prevent overfitting.
- The second convolutional block includes a Conv2D layer with 32 filters, a 3x3 kernel size, 'relu' activation, and 'same' padding, followed by a MaxPooling2D layer with a 2x2 pool size, 'same' padding, and another Dropout layer with a rate of 0.2.
- Similarly, the third convolutional block mirrors the second, with a Conv2D layer also having 32 filters, a 3x3 kernel size, 'relu' activation, and 'same' padding, a subsequent MaxPooling2D layer with a 2x2 pool size, 'same' padding, and a Dropout layer with a rate of 0.2.
- After extracting features through convolutional blocks, the model flattens the output to feed into fully connected layers for classification.
- The dense layers include a first Dense layer with 512 neurons and 'relu' activation, followed by a second Dense layer with 64 neurons and 'relu' activation, culminating in a final Dense layer with 4 neurons corresponding to the number of classes, using 'softmax' activation for multi-class classification.
- The model employs the Adam optimizer with a learning rate of 0.001 to adjust weights and minimize the loss function during training.
backend.clear_session()
# Fixing the seed for random number generators so that we can ensure we receive the same output everytime
np.random.seed(42)
random.seed(42)
tf.random.set_seed(42)
# Intializing a sequential model
model_1 = Sequential()
model_1.add(Input(shape=(img_width, img_height, color_layers)))
model_1.add(Conv2D(64, (3, 3), activation="relu", padding="same"))
model_1.add(MaxPooling2D((2, 2), padding="same"))
model_1.add(Dropout(0.2))
# Adding second conv layer with 32 filters
model_1.add(Conv2D(32, (3, 3), activation="relu", padding="same"))
model_1.add(MaxPooling2D((2, 2), padding="same"))
model_1.add(Dropout(0.2))
# Add third conv layer with 32 filters and kernel size 3x3, padding 'same' followed by a Maxpooling2D layer
model_1.add(Conv2D(32, (3, 3), activation="relu", padding="same"))
model_1.add(MaxPooling2D((2, 2), padding="same"))
model_1.add(Dropout(0.2))
# Flattening the output of the conv layer after max pooling to make it ready for creating dense connections
model_1.add(Flatten())
# Adding fully connected dense layers
model_1.add(Dense(512, activation="relu"))
model_1.add(Dense(64, activation="relu"))
# Adding output layer
model_1.add(Dense(4, activation="softmax"))
# Using Adam Optimizer
opt = Adam(learning_rate=0.001)
2024-04-11 00:04:11.726218: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-04-11 00:04:11.747212: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-04-11 00:04:11.747375: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-04-11 00:04:11.747960: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-04-11 00:04:11.748070: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-04-11 00:04:11.748171: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-04-11 00:04:11.802454: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-04-11 00:04:11.802581: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-04-11 00:04:11.802685: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-04-11 00:04:11.802760: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6272 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3070, pci bus id: 0000:06:00.0, compute capability: 8.6
Compiling and Training the Model¶
# Compiling the model
model_1.compile(optimizer=opt, loss="categorical_crossentropy", metrics=["accuracy"])
# Generating the summary of the model
model_1.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ conv2d (Conv2D) │ (None, 48, 48, 64) │ 640 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d (MaxPooling2D) │ (None, 24, 24, 64) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dropout (Dropout) │ (None, 24, 24, 64) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_1 (Conv2D) │ (None, 24, 24, 32) │ 18,464 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_1 (MaxPooling2D) │ (None, 12, 12, 32) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dropout_1 (Dropout) │ (None, 12, 12, 32) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_2 (Conv2D) │ (None, 12, 12, 32) │ 9,248 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_2 (MaxPooling2D) │ (None, 6, 6, 32) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dropout_2 (Dropout) │ (None, 6, 6, 32) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ flatten (Flatten) │ (None, 1152) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense (Dense) │ (None, 512) │ 590,336 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_1 (Dense) │ (None, 64) │ 32,832 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_2 (Dense) │ (None, 4) │ 260 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 651,780 (2.49 MB)
Trainable params: 651,780 (2.49 MB)
Non-trainable params: 0 (0.00 B)
results_path = "/home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results"
class DelayedEarlyStopping(EarlyStopping):
"""Stop training when a monitored metric has stopped improving after a certain number of epochs.
Arguments:
monitor: Quantity to be monitored.
min_delta: Minimum change in the monitored quantity to qualify as an improvement,
i.e., an absolute change of less than min_delta will count as no improvement.
patience: Number of epochs with no improvement after which training will be stopped.
verbose: Verbosity mode.
mode: One of `{'auto', 'min', 'max'}`. In `min` mode, training will stop when the
quantity monitored has stopped decreasing; in `max` mode it will stop when the
quantity monitored has stopped increasing; in `auto` mode, the direction is
automatically inferred from the name of the monitored quantity.
baseline: Baseline value for the monitored quantity. Training will stop if the model
doesn't show improvement over the baseline.
restore_best_weights: Whether to restore model weights from the epoch with the best value
of the monitored quantity.
start_epoch: The epoch on which to start considering early stopping. Before this epoch,
early stopping will not be considered. This ensures that early stopping
checks only after a certain number of epochs.
"""
def __init__(
self,
monitor="val_loss",
min_delta=0,
patience=0,
verbose=0,
mode="auto",
baseline=None,
restore_best_weights=False,
start_epoch=30,
):
super().__init__(
monitor=monitor,
min_delta=min_delta,
patience=patience,
verbose=verbose,
mode=mode,
baseline=baseline,
restore_best_weights=restore_best_weights,
)
self.start_epoch = start_epoch
def on_epoch_end(self, epoch, logs=None):
# Override the original `on_epoch_end` method to include `start_epoch` logic.
# If the current epoch is less than the start epoch, skip the early stopping check
if epoch < self.start_epoch:
return
# Call the parent class method to perform the regular early stopping checks after the start epoch
super().on_epoch_end(epoch, logs)
# Get the current time
current_time = datetime.now().strftime("%Y%m%d-%H%M%S")
# Set up Early Stopping with a patience 7 but acting after at least 30 epochs
delayed_early_stopping = DelayedEarlyStopping(
monitor="val_loss", patience=7, verbose=1, restore_best_weights=True, start_epoch=30
)
# Define the learning rate scheduler callback
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, patience=5, min_lr=0.00001, verbose=1)
# Define the saving the best model callback
mc = ModelCheckpoint(
f"{results_path}/best_model_1_{current_time}.keras",
monitor="val_accuracy",
mode="max",
verbose=1,
save_best_only=True,
)
# Fitting the model with 45 epochs and using validation set
history_1 = model_1.fit(
train_generator,
epochs=45,
validation_data=validation_generator,
callbacks=[reduce_lr, mc, delayed_early_stopping],
)
Epoch 1/45
/home/iamtxena/sandbox/mit-ai/my_env/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored. self._warn_if_super_not_called() WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1712793853.651159 1474977 service.cc:145] XLA service 0x7f226c00e760 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: I0000 00:00:1712793853.651180 1474977 service.cc:153] StreamExecutor device (0): NVIDIA GeForce RTX 3070, Compute Capability 8.6 2024-04-11 00:04:13.681751: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-04-11 00:04:13.811989: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8907
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1712793855.129443 1475073 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_1953', 4 bytes spill stores, 4 bytes spill loads
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I0000 00:00:1712793857.604780 1474977 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
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Epoch 1: val_accuracy improved from -inf to 0.37472, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 16s 24ms/step - accuracy: 0.2724 - loss: 1.3787 - val_accuracy: 0.3747 - val_loss: 1.2493 - learning_rate: 0.0010
Epoch 2/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.5000 - loss: 1.1636
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.4151 - loss: 1.2334
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.3976 - loss: 1.2453
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36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.3847 - loss: 1.2614
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210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.3717 - loss: 1.2821
215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.3718 - loss: 1.2821
220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.3719 - loss: 1.2820
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.3719 - loss: 1.2820
230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.3720 - loss: 1.2819
235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.3721 - loss: 1.2818
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.3722 - loss: 1.2817
245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.3722 - loss: 1.2817
250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.3723 - loss: 1.2816
255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.3724 - loss: 1.2815
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296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.3730 - loss: 1.2810
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306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3731 - loss: 1.2809
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3731 - loss: 1.2808
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3732 - loss: 1.2808
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3733 - loss: 1.2807
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3733 - loss: 1.2806
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3734 - loss: 1.2805
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3735 - loss: 1.2804
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3736 - loss: 1.2803
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3737 - loss: 1.2802
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3738 - loss: 1.2801
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3739 - loss: 1.2800
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3740 - loss: 1.2798
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3742 - loss: 1.2797
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3743 - loss: 1.2796
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.3744 - loss: 1.2794
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400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.3751 - loss: 1.2787
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435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.3760 - loss: 1.2776
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.3762 - loss: 1.2775
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.3763 - loss: 1.2773
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.3765 - loss: 1.2771
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.3766 - loss: 1.2770
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.3768 - loss: 1.2768
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Epoch 2: val_accuracy improved from 0.37472 to 0.47720, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.3772 - loss: 1.2763 - val_accuracy: 0.4772 - val_loss: 1.1544 - learning_rate: 0.0010
Epoch 3/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.3750 - loss: 1.2430
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.3927 - loss: 1.2220
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.4004 - loss: 1.2231
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.4089 - loss: 1.2233
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.4121 - loss: 1.2232
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51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.4110 - loss: 1.2267
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Epoch 3: val_accuracy improved from 0.47720 to 0.55053, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.4287 - loss: 1.2109 - val_accuracy: 0.5505 - val_loss: 1.0695 - learning_rate: 0.0010
Epoch 4/45
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Epoch 4: val_accuracy improved from 0.55053 to 0.58549, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.4784 - loss: 1.1409 - val_accuracy: 0.5855 - val_loss: 0.9747 - learning_rate: 0.0010
Epoch 5/45
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6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.4814 - loss: 1.1200
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Epoch 5: val_accuracy improved from 0.58549 to 0.58891, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5028 - loss: 1.0931 - val_accuracy: 0.5889 - val_loss: 0.9510 - learning_rate: 0.0010
Epoch 6/45
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6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.5552 - loss: 0.9879
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Epoch 6: val_accuracy improved from 0.58891 to 0.60137, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5267 - loss: 1.0534 - val_accuracy: 0.6014 - val_loss: 0.9226 - learning_rate: 0.0010
Epoch 7/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.7188 - loss: 0.7606
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443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5415 - loss: 1.0383
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5415 - loss: 1.0384
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5415 - loss: 1.0385
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5415 - loss: 1.0386
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Epoch 7: val_accuracy improved from 0.60137 to 0.63392, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5414 - loss: 1.0388 - val_accuracy: 0.6339 - val_loss: 0.8682 - learning_rate: 0.0010
Epoch 8/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 104ms/step - accuracy: 0.8125 - loss: 0.7424
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6265 - loss: 0.9412
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6129 - loss: 0.9620
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6055 - loss: 0.9678
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5996 - loss: 0.9705
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Epoch 8: val_accuracy improved from 0.63392 to 0.64657, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5506 - loss: 1.0149 - val_accuracy: 0.6466 - val_loss: 0.8343 - learning_rate: 0.0010
Epoch 9/45
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Epoch 9: val_accuracy improved from 0.64657 to 0.64838, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5641 - loss: 0.9965 - val_accuracy: 0.6484 - val_loss: 0.8419 - learning_rate: 0.0010
Epoch 10/45
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345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5845 - loss: 0.9732
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355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5843 - loss: 0.9734
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5843 - loss: 0.9734
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5842 - loss: 0.9735
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5841 - loss: 0.9736
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5840 - loss: 0.9736
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5840 - loss: 0.9737
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5839 - loss: 0.9738
390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5839 - loss: 0.9738
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5838 - loss: 0.9738
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5838 - loss: 0.9739
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5837 - loss: 0.9739
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414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5836 - loss: 0.9740
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424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5835 - loss: 0.9741
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5835 - loss: 0.9742
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5834 - loss: 0.9743
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5833 - loss: 0.9743
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5833 - loss: 0.9744
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5832 - loss: 0.9744
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5831 - loss: 0.9745
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5831 - loss: 0.9746
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5830 - loss: 0.9746
Epoch 10: val_accuracy did not improve from 0.64838
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5828 - loss: 0.9748 - val_accuracy: 0.6383 - val_loss: 0.8619 - learning_rate: 0.0010
Epoch 11/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.5000 - loss: 1.0313
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5612 - loss: 1.0064
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5735 - loss: 0.9932
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5725 - loss: 0.9904
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5713 - loss: 0.9871
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5715 - loss: 0.9857
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5711 - loss: 0.9852
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5707 - loss: 0.9856
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5697 - loss: 0.9870
46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5689 - loss: 0.9885
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5686 - loss: 0.9895
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5687 - loss: 0.9897
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5690 - loss: 0.9893
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5695 - loss: 0.9889
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5700 - loss: 0.9886
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79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5709 - loss: 0.9882
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5713 - loss: 0.9882
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5717 - loss: 0.9882
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5721 - loss: 0.9882
98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5724 - loss: 0.9883
102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5727 - loss: 0.9884
107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5732 - loss: 0.9885
112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5737 - loss: 0.9885
117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5742 - loss: 0.9884
122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5747 - loss: 0.9882
127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5752 - loss: 0.9880
132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5757 - loss: 0.9878
137/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5761 - loss: 0.9875
142/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5765 - loss: 0.9873
146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5768 - loss: 0.9871
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5771 - loss: 0.9869
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5775 - loss: 0.9867
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5778 - loss: 0.9865
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5781 - loss: 0.9863
170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5784 - loss: 0.9861
175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5786 - loss: 0.9859
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5788 - loss: 0.9857
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5790 - loss: 0.9855
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5792 - loss: 0.9853
195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5794 - loss: 0.9852
200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5795 - loss: 0.9850
205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5797 - loss: 0.9848
210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5798 - loss: 0.9847
215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5799 - loss: 0.9846
220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5799 - loss: 0.9844
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5799 - loss: 0.9843
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5800 - loss: 0.9842
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5799 - loss: 0.9841
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5799 - loss: 0.9840
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5799 - loss: 0.9839
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5799 - loss: 0.9838
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9837
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266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9835
271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9834
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9833
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9831
285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9830
290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9829
295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9828
300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9826
305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5798 - loss: 0.9825
310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5798 - loss: 0.9823
315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5798 - loss: 0.9822
320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5799 - loss: 0.9821
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5799 - loss: 0.9819
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5799 - loss: 0.9818
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5799 - loss: 0.9816
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5800 - loss: 0.9815
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5800 - loss: 0.9813
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5800 - loss: 0.9812
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5800 - loss: 0.9810
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5800 - loss: 0.9809
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5801 - loss: 0.9807
368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5801 - loss: 0.9806
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5801 - loss: 0.9804
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5802 - loss: 0.9802
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5802 - loss: 0.9800
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5803 - loss: 0.9799
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5803 - loss: 0.9797
397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5803 - loss: 0.9796
402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5803 - loss: 0.9794
407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5804 - loss: 0.9793
412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5804 - loss: 0.9792
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5804 - loss: 0.9791
421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5804 - loss: 0.9789
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5804 - loss: 0.9788
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5805 - loss: 0.9787
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5805 - loss: 0.9785
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5805 - loss: 0.9784
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5805 - loss: 0.9783
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5805 - loss: 0.9782
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5805 - loss: 0.9781
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5805 - loss: 0.9780
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5805 - loss: 0.9779
Epoch 11: val_accuracy improved from 0.64838 to 0.65943, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5805 - loss: 0.9778 - val_accuracy: 0.6594 - val_loss: 0.8030 - learning_rate: 0.0010
Epoch 12/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.6562 - loss: 0.7645
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Epoch 12: val_accuracy improved from 0.65943 to 0.66667, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5918 - loss: 0.9552 - val_accuracy: 0.6667 - val_loss: 0.7943 - learning_rate: 0.0010
Epoch 13/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 55s 119ms/step - accuracy: 0.7812 - loss: 0.7791
4/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.6719 - loss: 0.8903
9/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.6104 - loss: 0.9649
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Epoch 13: val_accuracy improved from 0.66667 to 0.67109, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5927 - loss: 0.9443 - val_accuracy: 0.6711 - val_loss: 0.8001 - learning_rate: 0.0010
Epoch 14/45
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250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5966 - loss: 0.9490
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260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5968 - loss: 0.9487
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5969 - loss: 0.9486
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5970 - loss: 0.9485
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5970 - loss: 0.9484
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5971 - loss: 0.9483
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5972 - loss: 0.9482
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5972 - loss: 0.9481
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5973 - loss: 0.9480
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5973 - loss: 0.9479
303/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5974 - loss: 0.9479
308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5974 - loss: 0.9478
313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5974 - loss: 0.9478
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5975 - loss: 0.9477
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5975 - loss: 0.9477
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5975 - loss: 0.9476
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5975 - loss: 0.9475
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9474
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9474
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9473
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9473
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9472
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9472
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9471
368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5977 - loss: 0.9470
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5977 - loss: 0.9470
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5977 - loss: 0.9469
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5977 - loss: 0.9468
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5977 - loss: 0.9468
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5977 - loss: 0.9467
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5977 - loss: 0.9467
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5977 - loss: 0.9466
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5978 - loss: 0.9465
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5978 - loss: 0.9465
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5978 - loss: 0.9464
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5978 - loss: 0.9463
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5978 - loss: 0.9462
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5978 - loss: 0.9461
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5979 - loss: 0.9461
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5979 - loss: 0.9460
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5979 - loss: 0.9459
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5979 - loss: 0.9458
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5980 - loss: 0.9457
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5980 - loss: 0.9457
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5980 - loss: 0.9456
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5980 - loss: 0.9455
Epoch 14: val_accuracy did not improve from 0.67109
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5980 - loss: 0.9454 - val_accuracy: 0.6649 - val_loss: 0.7973 - learning_rate: 0.0010
Epoch 15/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.5625 - loss: 0.9892
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5883 - loss: 0.9528
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5941 - loss: 0.9413
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5969 - loss: 0.9385
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5983 - loss: 0.9376
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5988 - loss: 0.9390
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5991 - loss: 0.9393
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5992 - loss: 0.9389
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5994 - loss: 0.9377
46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5992 - loss: 0.9363
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5992 - loss: 0.9356
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5989 - loss: 0.9352
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5990 - loss: 0.9346
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5989 - loss: 0.9343
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5989 - loss: 0.9340
77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5987 - loss: 0.9338
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5985 - loss: 0.9340
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5984 - loss: 0.9343
87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5982 - loss: 0.9346
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5981 - loss: 0.9348
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5978 - loss: 0.9351
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5977 - loss: 0.9351
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5977 - loss: 0.9350
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5977 - loss: 0.9350
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5976 - loss: 0.9350
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5975 - loss: 0.9351
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5975 - loss: 0.9350
130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5975 - loss: 0.9349
135/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5977 - loss: 0.9346
140/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5978 - loss: 0.9343
144/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5979 - loss: 0.9340
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5980 - loss: 0.9338
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5980 - loss: 0.9338
158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5981 - loss: 0.9337
163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5981 - loss: 0.9337
168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5981 - loss: 0.9337
173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5980 - loss: 0.9338
178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5980 - loss: 0.9338
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5981 - loss: 0.9338
188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5982 - loss: 0.9337
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5982 - loss: 0.9337
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5983 - loss: 0.9337
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5983 - loss: 0.9337
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5983 - loss: 0.9337
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5983 - loss: 0.9337
218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5983 - loss: 0.9338
223/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5983 - loss: 0.9337
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5983 - loss: 0.9337
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5984 - loss: 0.9337
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5984 - loss: 0.9337
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5984 - loss: 0.9337
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5985 - loss: 0.9337
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5985 - loss: 0.9336
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5985 - loss: 0.9336
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5985 - loss: 0.9337
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5985 - loss: 0.9337
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5986 - loss: 0.9337
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5986 - loss: 0.9336
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5986 - loss: 0.9336
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5987 - loss: 0.9336
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5987 - loss: 0.9335
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5988 - loss: 0.9335
302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5988 - loss: 0.9334
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5989 - loss: 0.9333
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5989 - loss: 0.9332
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5990 - loss: 0.9332
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5990 - loss: 0.9331
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5991 - loss: 0.9330
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5991 - loss: 0.9329
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5992 - loss: 0.9329
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5992 - loss: 0.9328
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5992 - loss: 0.9328
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5992 - loss: 0.9327
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5992 - loss: 0.9326
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5993 - loss: 0.9326
367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5993 - loss: 0.9326
372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5993 - loss: 0.9325
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Epoch 15: val_accuracy improved from 0.67109 to 0.68093, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5998 - loss: 0.9322 - val_accuracy: 0.6809 - val_loss: 0.7717 - learning_rate: 0.0010
Epoch 16/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.5938 - loss: 0.9472
5/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.5870 - loss: 0.9426
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325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5973 - loss: 0.9259
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5974 - loss: 0.9258
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9257
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5977 - loss: 0.9256
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440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5996 - loss: 0.9249
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5996 - loss: 0.9249
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5997 - loss: 0.9249
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5998 - loss: 0.9249
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5998 - loss: 0.9249
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Epoch 16: val_accuracy improved from 0.68093 to 0.68877, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6000 - loss: 0.9249 - val_accuracy: 0.6888 - val_loss: 0.7655 - learning_rate: 0.0010
Epoch 17/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 46s 99ms/step - accuracy: 0.5000 - loss: 0.8790
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6091 - loss: 0.8700
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6230 - loss: 0.8815
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6284 - loss: 0.8876
19/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6294 - loss: 0.8899
22/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6304 - loss: 0.8914
25/473 ━━━━━━━━━━━━━━━━━━━━ 7s 16ms/step - accuracy: 0.6315 - loss: 0.8914
27/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.6322 - loss: 0.8914
30/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.6339 - loss: 0.8906
35/473 ━━━━━━━━━━━━━━━━━━━━ 7s 16ms/step - accuracy: 0.6348 - loss: 0.8904
39/473 ━━━━━━━━━━━━━━━━━━━━ 6s 16ms/step - accuracy: 0.6351 - loss: 0.8899
44/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6346 - loss: 0.8912
48/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6340 - loss: 0.8919
52/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6338 - loss: 0.8921
56/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6336 - loss: 0.8922
60/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6331 - loss: 0.8927
65/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6324 - loss: 0.8934
70/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6319 - loss: 0.8941
74/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6315 - loss: 0.8946
79/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6310 - loss: 0.8953
84/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6306 - loss: 0.8960
89/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6302 - loss: 0.8967
94/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6298 - loss: 0.8974
98/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6294 - loss: 0.8981
102/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6290 - loss: 0.8989
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.6285 - loss: 0.8997
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6278 - loss: 0.9007
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6272 - loss: 0.9016
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6267 - loss: 0.9022
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6263 - loss: 0.9027
130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6259 - loss: 0.9033
135/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6256 - loss: 0.9037
139/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6253 - loss: 0.9040
143/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6250 - loss: 0.9043
147/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6248 - loss: 0.9045
152/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6244 - loss: 0.9048
157/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6241 - loss: 0.9052
161/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6238 - loss: 0.9055
166/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6234 - loss: 0.9058
171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6230 - loss: 0.9062
175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6228 - loss: 0.9064
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6225 - loss: 0.9065
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6223 - loss: 0.9067
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6220 - loss: 0.9068
195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6218 - loss: 0.9069
200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6216 - loss: 0.9070
205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6214 - loss: 0.9072
210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6212 - loss: 0.9073
215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6209 - loss: 0.9074
219/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6207 - loss: 0.9076
224/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6205 - loss: 0.9077
229/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6203 - loss: 0.9078
234/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6200 - loss: 0.9079
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6199 - loss: 0.9080
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6197 - loss: 0.9080
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6196 - loss: 0.9080
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6195 - loss: 0.9079
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6194 - loss: 0.9079
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6193 - loss: 0.9079
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6192 - loss: 0.9078
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6191 - loss: 0.9078
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6190 - loss: 0.9078
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6189 - loss: 0.9078
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6189 - loss: 0.9077
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6188 - loss: 0.9077
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6187 - loss: 0.9077
303/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6187 - loss: 0.9077
308/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6186 - loss: 0.9077
313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6185 - loss: 0.9077
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6185 - loss: 0.9077
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6184 - loss: 0.9076
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6183 - loss: 0.9076
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6182 - loss: 0.9076
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6181 - loss: 0.9076
343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6181 - loss: 0.9076
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6180 - loss: 0.9076
353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6179 - loss: 0.9076
358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6178 - loss: 0.9077
363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6178 - loss: 0.9077
368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6177 - loss: 0.9077
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6176 - loss: 0.9077
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6176 - loss: 0.9077
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6175 - loss: 0.9077
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6174 - loss: 0.9077
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6173 - loss: 0.9077
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6173 - loss: 0.9077
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6172 - loss: 0.9077
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6171 - loss: 0.9076
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6171 - loss: 0.9076
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6170 - loss: 0.9076
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6170 - loss: 0.9076
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6169 - loss: 0.9076
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6168 - loss: 0.9076
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6168 - loss: 0.9076
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6167 - loss: 0.9076
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6167 - loss: 0.9076
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6166 - loss: 0.9076
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6166 - loss: 0.9077
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6165 - loss: 0.9077
Epoch 17: val_accuracy did not improve from 0.68877
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6163 - loss: 0.9077 - val_accuracy: 0.6711 - val_loss: 0.7998 - learning_rate: 0.0010
Epoch 18/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.7188 - loss: 0.7676
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6908 - loss: 0.8521
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6790 - loss: 0.8791
17/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6642 - loss: 0.8981
22/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6554 - loss: 0.9052
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6507 - loss: 0.9086
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6463 - loss: 0.9112
34/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6425 - loss: 0.9127
39/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6385 - loss: 0.9130
44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6354 - loss: 0.9133
48/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6339 - loss: 0.9127
53/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6329 - loss: 0.9113
58/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6317 - loss: 0.9102
63/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6302 - loss: 0.9094
68/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6288 - loss: 0.9087
73/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6276 - loss: 0.9080
78/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6265 - loss: 0.9075
83/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6256 - loss: 0.9071
88/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6247 - loss: 0.9070
93/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6239 - loss: 0.9067
98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6231 - loss: 0.9066
103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6225 - loss: 0.9065
108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6219 - loss: 0.9063
113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6213 - loss: 0.9063
118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6207 - loss: 0.9065
123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6203 - loss: 0.9067
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133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6196 - loss: 0.9067
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Epoch 18: val_accuracy improved from 0.68877 to 0.69078, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6185 - loss: 0.9040 - val_accuracy: 0.6908 - val_loss: 0.7475 - learning_rate: 0.0010
Epoch 19/45
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253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6302 - loss: 0.8895
257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6301 - loss: 0.8896
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6300 - loss: 0.8897
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6299 - loss: 0.8898
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6298 - loss: 0.8899
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6297 - loss: 0.8900
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6296 - loss: 0.8901
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6295 - loss: 0.8901
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6294 - loss: 0.8902
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6293 - loss: 0.8903
302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6292 - loss: 0.8904
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6290 - loss: 0.8905
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6289 - loss: 0.8906
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6288 - loss: 0.8907
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6286 - loss: 0.8908
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6285 - loss: 0.8909
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6284 - loss: 0.8910
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6283 - loss: 0.8911
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6283 - loss: 0.8912
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6282 - loss: 0.8913
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6281 - loss: 0.8913
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6280 - loss: 0.8914
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6279 - loss: 0.8914
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6279 - loss: 0.8914
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6278 - loss: 0.8915
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6277 - loss: 0.8915
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6277 - loss: 0.8915
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6276 - loss: 0.8915
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6276 - loss: 0.8915
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6275 - loss: 0.8915
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6275 - loss: 0.8915
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6274 - loss: 0.8915
412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6274 - loss: 0.8915
417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6273 - loss: 0.8915
422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6273 - loss: 0.8915
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6273 - loss: 0.8915
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6272 - loss: 0.8915
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6272 - loss: 0.8915
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6271 - loss: 0.8916
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6271 - loss: 0.8916
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6271 - loss: 0.8916
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6271 - loss: 0.8916
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6270 - loss: 0.8916
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6270 - loss: 0.8916
Epoch 19: val_accuracy did not improve from 0.69078
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6269 - loss: 0.8916 - val_accuracy: 0.6856 - val_loss: 0.7629 - learning_rate: 0.0010
Epoch 20/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 46s 98ms/step - accuracy: 0.5625 - loss: 0.9138
5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5675 - loss: 0.9293
8/473 ━━━━━━━━━━━━━━━━━━━━ 8s 17ms/step - accuracy: 0.5744 - loss: 0.9145
11/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5805 - loss: 0.9089
16/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.5872 - loss: 0.9070
21/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.5899 - loss: 0.9064
26/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.5909 - loss: 0.9059
31/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.5938 - loss: 0.9028
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.5964 - loss: 0.8991
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.5990 - loss: 0.8958
46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6010 - loss: 0.8936
51/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6024 - loss: 0.8925
56/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6034 - loss: 0.8919
60/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6040 - loss: 0.8914
65/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6048 - loss: 0.8908
70/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6055 - loss: 0.8902
75/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6061 - loss: 0.8900
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6064 - loss: 0.8901
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6066 - loss: 0.8906
88/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6068 - loss: 0.8907
93/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6072 - loss: 0.8908
98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6075 - loss: 0.8910
103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6078 - loss: 0.8911
108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6080 - loss: 0.8914
113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6081 - loss: 0.8916
118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6083 - loss: 0.8917
123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6085 - loss: 0.8919
128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6087 - loss: 0.8921
133/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6089 - loss: 0.8922
138/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6092 - loss: 0.8924
143/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6094 - loss: 0.8925
148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6098 - loss: 0.8925
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6102 - loss: 0.8923
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6105 - loss: 0.8922
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6109 - loss: 0.8920
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6112 - loss: 0.8918
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6115 - loss: 0.8916
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6118 - loss: 0.8914
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6120 - loss: 0.8912
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6122 - loss: 0.8911
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6124 - loss: 0.8909
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6125 - loss: 0.8908
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6127 - loss: 0.8907
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6128 - loss: 0.8906
214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6130 - loss: 0.8905
219/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6131 - loss: 0.8905
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6132 - loss: 0.8904
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6134 - loss: 0.8904
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6136 - loss: 0.8903
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6138 - loss: 0.8902
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6139 - loss: 0.8902
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6141 - loss: 0.8901
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6143 - loss: 0.8900
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6144 - loss: 0.8899
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6146 - loss: 0.8898
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6148 - loss: 0.8897
274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6149 - loss: 0.8897
279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6151 - loss: 0.8896
284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6152 - loss: 0.8896
289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6153 - loss: 0.8895
294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6154 - loss: 0.8895
299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6155 - loss: 0.8894
304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6156 - loss: 0.8894
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6157 - loss: 0.8894
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6158 - loss: 0.8894
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6159 - loss: 0.8894
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6160 - loss: 0.8894
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6160 - loss: 0.8894
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6161 - loss: 0.8895
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6161 - loss: 0.8895
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6162 - loss: 0.8896
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6162 - loss: 0.8896
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6162 - loss: 0.8896
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6163 - loss: 0.8897
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6163 - loss: 0.8897
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6164 - loss: 0.8897
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6165 - loss: 0.8897
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6165 - loss: 0.8897
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6166 - loss: 0.8897
389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6166 - loss: 0.8897
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399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6167 - loss: 0.8898
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409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6168 - loss: 0.8899
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438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6169 - loss: 0.8901
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6170 - loss: 0.8902
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453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6170 - loss: 0.8902
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Epoch 20: val_accuracy did not improve from 0.69078
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6171 - loss: 0.8903 - val_accuracy: 0.6872 - val_loss: 0.7617 - learning_rate: 0.0010
Epoch 21/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.7500 - loss: 0.6629
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6591 - loss: 0.7993
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6492 - loss: 0.8362
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6465 - loss: 0.8480
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6440 - loss: 0.8541
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6448 - loss: 0.8542
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91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6349 - loss: 0.8633
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6345 - loss: 0.8642
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6341 - loss: 0.8650
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6337 - loss: 0.8657
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144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6307 - loss: 0.8694
147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6304 - loss: 0.8697
152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6301 - loss: 0.8701
157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6297 - loss: 0.8706
161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6295 - loss: 0.8710
166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6292 - loss: 0.8714
171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6289 - loss: 0.8717
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181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6284 - loss: 0.8723
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6282 - loss: 0.8727
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201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6275 - loss: 0.8735
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6273 - loss: 0.8737
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6271 - loss: 0.8739
216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6270 - loss: 0.8741
221/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6268 - loss: 0.8743
226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6267 - loss: 0.8746
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6265 - loss: 0.8748
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6264 - loss: 0.8749
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6263 - loss: 0.8751
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6262 - loss: 0.8753
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264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6259 - loss: 0.8759
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6258 - loss: 0.8760
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6258 - loss: 0.8761
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6257 - loss: 0.8763
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6256 - loss: 0.8764
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6256 - loss: 0.8765
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6255 - loss: 0.8767
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6255 - loss: 0.8768
302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6255 - loss: 0.8769
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6254 - loss: 0.8770
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6254 - loss: 0.8771
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6253 - loss: 0.8772
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6253 - loss: 0.8773
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6253 - loss: 0.8774
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6253 - loss: 0.8775
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8775
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8776
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8776
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8777
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8777
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8778
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8779
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371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8780
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8780
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8781
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391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6251 - loss: 0.8782
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6251 - loss: 0.8782
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6251 - loss: 0.8783
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437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6250 - loss: 0.8785
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6250 - loss: 0.8786
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6250 - loss: 0.8786
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6250 - loss: 0.8787
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6249 - loss: 0.8787
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6249 - loss: 0.8788
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6249 - loss: 0.8789
Epoch 21: val_accuracy improved from 0.69078 to 0.69982, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6249 - loss: 0.8789 - val_accuracy: 0.6998 - val_loss: 0.7321 - learning_rate: 0.0010
Epoch 22/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 46s 99ms/step - accuracy: 0.6562 - loss: 0.8259
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6773 - loss: 0.8589
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6701 - loss: 0.8581
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6644 - loss: 0.8631
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Epoch 22: val_accuracy improved from 0.69982 to 0.70404, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6323 - loss: 0.8813 - val_accuracy: 0.7040 - val_loss: 0.7202 - learning_rate: 0.0010
Epoch 23/45
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138/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6387 - loss: 0.8499
143/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6385 - loss: 0.8506
148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6383 - loss: 0.8512
153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6382 - loss: 0.8518
158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6379 - loss: 0.8524
161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6378 - loss: 0.8527
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6377 - loss: 0.8530
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6376 - loss: 0.8534
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6374 - loss: 0.8538
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6372 - loss: 0.8541
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6371 - loss: 0.8544
188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6368 - loss: 0.8549
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6366 - loss: 0.8555
197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6364 - loss: 0.8559
202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6362 - loss: 0.8564
207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6360 - loss: 0.8568
212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6358 - loss: 0.8573
217/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6355 - loss: 0.8578
222/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6353 - loss: 0.8582
227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6352 - loss: 0.8586
232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6350 - loss: 0.8589
237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6349 - loss: 0.8592
242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6347 - loss: 0.8595
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6346 - loss: 0.8599
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6344 - loss: 0.8602
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6343 - loss: 0.8606
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6341 - loss: 0.8609
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6340 - loss: 0.8613
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6339 - loss: 0.8616
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6337 - loss: 0.8619
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6336 - loss: 0.8623
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6334 - loss: 0.8626
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6333 - loss: 0.8629
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6331 - loss: 0.8632
302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6330 - loss: 0.8636
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6328 - loss: 0.8639
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6327 - loss: 0.8641
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6326 - loss: 0.8644
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6325 - loss: 0.8647
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6323 - loss: 0.8649
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6322 - loss: 0.8652
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6321 - loss: 0.8655
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6320 - loss: 0.8657
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6319 - loss: 0.8660
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6318 - loss: 0.8662
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6317 - loss: 0.8665
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6316 - loss: 0.8667
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6314 - loss: 0.8670
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6313 - loss: 0.8672
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6312 - loss: 0.8675
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6311 - loss: 0.8677
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6310 - loss: 0.8679
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6309 - loss: 0.8681
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6308 - loss: 0.8684
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6307 - loss: 0.8686
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6306 - loss: 0.8688
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6305 - loss: 0.8690
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6304 - loss: 0.8691
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6303 - loss: 0.8693
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6303 - loss: 0.8695
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6302 - loss: 0.8697
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6301 - loss: 0.8698
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6300 - loss: 0.8700
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6300 - loss: 0.8702
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6299 - loss: 0.8703
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6299 - loss: 0.8705
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6298 - loss: 0.8706
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6298 - loss: 0.8708
Epoch 23: val_accuracy did not improve from 0.70404
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6297 - loss: 0.8711 - val_accuracy: 0.6918 - val_loss: 0.7534 - learning_rate: 0.0010
Epoch 24/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.6875 - loss: 0.8393
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6451 - loss: 0.8248
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6421 - loss: 0.8249
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6387 - loss: 0.8284
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6373 - loss: 0.8303
24/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6377 - loss: 0.8315
28/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6394 - loss: 0.8318
33/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6403 - loss: 0.8339
38/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6403 - loss: 0.8370
42/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6402 - loss: 0.8394
46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6399 - loss: 0.8423
51/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6399 - loss: 0.8452
56/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6400 - loss: 0.8470
60/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6403 - loss: 0.8478
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6408 - loss: 0.8481
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6415 - loss: 0.8480
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6418 - loss: 0.8482
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6420 - loss: 0.8487
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6420 - loss: 0.8490
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6421 - loss: 0.8491
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6421 - loss: 0.8490
97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6422 - loss: 0.8488
102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6423 - loss: 0.8486
107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6423 - loss: 0.8486
112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6424 - loss: 0.8485
117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6425 - loss: 0.8485
122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6425 - loss: 0.8486
127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6424 - loss: 0.8488
132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6423 - loss: 0.8490
137/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6422 - loss: 0.8492
142/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6422 - loss: 0.8494
147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6421 - loss: 0.8496
152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6421 - loss: 0.8499
157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6421 - loss: 0.8501
162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6421 - loss: 0.8504
167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6421 - loss: 0.8506
172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6420 - loss: 0.8509
177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6420 - loss: 0.8512
182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6419 - loss: 0.8514
187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6419 - loss: 0.8516
192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6418 - loss: 0.8518
197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6418 - loss: 0.8520
202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6417 - loss: 0.8522
207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6416 - loss: 0.8524
212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6416 - loss: 0.8526
217/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6415 - loss: 0.8527
222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6415 - loss: 0.8529
227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6415 - loss: 0.8530
232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6415 - loss: 0.8531
237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6415 - loss: 0.8532
242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6414 - loss: 0.8534
247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6414 - loss: 0.8536
252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6413 - loss: 0.8537
257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6413 - loss: 0.8539
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6412 - loss: 0.8541
266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6412 - loss: 0.8542
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Epoch 24: val_accuracy improved from 0.70404 to 0.70605, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6372 - loss: 0.8614 - val_accuracy: 0.7060 - val_loss: 0.7095 - learning_rate: 0.0010
Epoch 25/45
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Epoch 25: val_accuracy improved from 0.70605 to 0.70645, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6381 - loss: 0.8523 - val_accuracy: 0.7064 - val_loss: 0.7252 - learning_rate: 0.0010
Epoch 26/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.7188 - loss: 0.6251
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7061 - loss: 0.7188
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6823 - loss: 0.7583
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6687 - loss: 0.7773
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6630 - loss: 0.7915
27/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6605 - loss: 0.8007
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301/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6401 - loss: 0.8443
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311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6400 - loss: 0.8446
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6400 - loss: 0.8448
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6400 - loss: 0.8450
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6399 - loss: 0.8451
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6399 - loss: 0.8453
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6398 - loss: 0.8455
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6397 - loss: 0.8457
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356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6395 - loss: 0.8462
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396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6391 - loss: 0.8474
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6391 - loss: 0.8476
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411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6390 - loss: 0.8478
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421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6390 - loss: 0.8481
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431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6389 - loss: 0.8483
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6389 - loss: 0.8484
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6389 - loss: 0.8485
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6389 - loss: 0.8486
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6388 - loss: 0.8487
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6388 - loss: 0.8487
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6388 - loss: 0.8489
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6388 - loss: 0.8489
Epoch 26: val_accuracy did not improve from 0.70645
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6387 - loss: 0.8491 - val_accuracy: 0.7016 - val_loss: 0.7328 - learning_rate: 0.0010
Epoch 27/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 102ms/step - accuracy: 0.5938 - loss: 0.9629
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5549 - loss: 0.9478
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5750 - loss: 0.9179
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5877 - loss: 0.9068
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5955 - loss: 0.9003
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Epoch 27: val_accuracy improved from 0.70645 to 0.71087, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6416 - loss: 0.8574 - val_accuracy: 0.7109 - val_loss: 0.7201 - learning_rate: 0.0010
Epoch 28/45
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Epoch 28: val_accuracy improved from 0.71087 to 0.71167, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6445 - loss: 0.8524 - val_accuracy: 0.7117 - val_loss: 0.7050 - learning_rate: 0.0010
Epoch 29/45
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286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6462 - loss: 0.8353
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296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6461 - loss: 0.8356
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6461 - loss: 0.8357
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6460 - loss: 0.8359
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6459 - loss: 0.8360
315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6459 - loss: 0.8361
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6459 - loss: 0.8363
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6458 - loss: 0.8364
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6457 - loss: 0.8366
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6457 - loss: 0.8367
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6456 - loss: 0.8369
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6456 - loss: 0.8370
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6455 - loss: 0.8372
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6455 - loss: 0.8373
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6454 - loss: 0.8375
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6453 - loss: 0.8376
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6453 - loss: 0.8378
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6452 - loss: 0.8379
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6452 - loss: 0.8381
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6451 - loss: 0.8382
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6451 - loss: 0.8384
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6450 - loss: 0.8385
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6449 - loss: 0.8387
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6449 - loss: 0.8389
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6448 - loss: 0.8390
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6447 - loss: 0.8392
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6447 - loss: 0.8393
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6446 - loss: 0.8394
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6446 - loss: 0.8396
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6445 - loss: 0.8397
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6445 - loss: 0.8398
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6444 - loss: 0.8399
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6444 - loss: 0.8400
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6443 - loss: 0.8402
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6443 - loss: 0.8402
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6443 - loss: 0.8403
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6442 - loss: 0.8404
Epoch 29: val_accuracy did not improve from 0.71167
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6442 - loss: 0.8406 - val_accuracy: 0.7099 - val_loss: 0.7116 - learning_rate: 0.0010
Epoch 30/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 45s 97ms/step - accuracy: 0.6250 - loss: 1.0577
5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6103 - loss: 0.9443
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6072 - loss: 0.9184
13/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6094 - loss: 0.9118
16/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.6121 - loss: 0.9075
19/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.6150 - loss: 0.9035
24/473 ━━━━━━━━━━━━━━━━━━━━ 7s 16ms/step - accuracy: 0.6196 - loss: 0.8973
30/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6232 - loss: 0.8878
35/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.6244 - loss: 0.8804
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6250 - loss: 0.8754
45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6255 - loss: 0.8714
50/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6259 - loss: 0.8682
55/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6264 - loss: 0.8660
60/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6271 - loss: 0.8639
65/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6279 - loss: 0.8624
70/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6284 - loss: 0.8613
75/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6285 - loss: 0.8607
80/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6286 - loss: 0.8601
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6288 - loss: 0.8594
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6292 - loss: 0.8585
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6295 - loss: 0.8578
98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6297 - loss: 0.8575
102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6300 - loss: 0.8570
107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6303 - loss: 0.8563
112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6304 - loss: 0.8558
117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6304 - loss: 0.8556
122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6305 - loss: 0.8553
127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6306 - loss: 0.8550
131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6307 - loss: 0.8547
136/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6310 - loss: 0.8542
140/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6311 - loss: 0.8539
145/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6313 - loss: 0.8535
149/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6313 - loss: 0.8532
154/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6314 - loss: 0.8529
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6314 - loss: 0.8528
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6314 - loss: 0.8527
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6315 - loss: 0.8526
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6315 - loss: 0.8526
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6315 - loss: 0.8525
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8524
188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8523
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8522
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8521
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8520
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8519
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8519
218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6313 - loss: 0.8518
223/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6313 - loss: 0.8517
228/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6313 - loss: 0.8517
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6313 - loss: 0.8516
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6313 - loss: 0.8516
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6313 - loss: 0.8516
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6313 - loss: 0.8516
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6314 - loss: 0.8515
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6314 - loss: 0.8515
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6314 - loss: 0.8515
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6314 - loss: 0.8514
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6314 - loss: 0.8514
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6314 - loss: 0.8513
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6315 - loss: 0.8512
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6315 - loss: 0.8512
290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6316 - loss: 0.8511
294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6316 - loss: 0.8511
299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6317 - loss: 0.8510
304/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6317 - loss: 0.8510
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6318 - loss: 0.8509
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6318 - loss: 0.8508
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6319 - loss: 0.8507
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6319 - loss: 0.8506
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6320 - loss: 0.8505
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6321 - loss: 0.8504
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6322 - loss: 0.8503
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6323 - loss: 0.8502
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6324 - loss: 0.8501
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6324 - loss: 0.8500
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6325 - loss: 0.8499
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6326 - loss: 0.8498
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6327 - loss: 0.8497
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6328 - loss: 0.8496
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6328 - loss: 0.8496
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6329 - loss: 0.8495
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6330 - loss: 0.8494
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6331 - loss: 0.8494
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6332 - loss: 0.8493
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6333 - loss: 0.8493
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6333 - loss: 0.8492
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Epoch 30: val_accuracy improved from 0.71167 to 0.71529, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6346 - loss: 0.8485 - val_accuracy: 0.7153 - val_loss: 0.7097 - learning_rate: 0.0010
Epoch 31/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.6562 - loss: 0.6943
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445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6443 - loss: 0.8359
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Epoch 31: val_accuracy improved from 0.71529 to 0.72072, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6445 - loss: 0.8358 - val_accuracy: 0.7207 - val_loss: 0.6981 - learning_rate: 0.0010
Epoch 32/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 46s 99ms/step - accuracy: 0.7188 - loss: 0.7531
5/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7017 - loss: 0.7159
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6952 - loss: 0.7281
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19/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6903 - loss: 0.7464
24/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6873 - loss: 0.7553
29/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6855 - loss: 0.7612
34/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6836 - loss: 0.7670
39/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6824 - loss: 0.7713
44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6814 - loss: 0.7744
49/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6800 - loss: 0.7776
54/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6785 - loss: 0.7810
59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6769 - loss: 0.7839
64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6754 - loss: 0.7868
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6740 - loss: 0.7894
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6727 - loss: 0.7915
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6715 - loss: 0.7938
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6703 - loss: 0.7962
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6693 - loss: 0.7983
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6683 - loss: 0.8004
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6674 - loss: 0.8024
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6665 - loss: 0.8043
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6656 - loss: 0.8061
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6648 - loss: 0.8077
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6641 - loss: 0.8093
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6634 - loss: 0.8108
129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6628 - loss: 0.8122
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6623 - loss: 0.8135
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6618 - loss: 0.8146
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6614 - loss: 0.8156
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6609 - loss: 0.8167
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6604 - loss: 0.8178
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6600 - loss: 0.8188
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6596 - loss: 0.8198
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6592 - loss: 0.8207
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6588 - loss: 0.8215
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6584 - loss: 0.8222
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6581 - loss: 0.8229
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6578 - loss: 0.8234
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6576 - loss: 0.8239
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6574 - loss: 0.8243
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6572 - loss: 0.8247
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6570 - loss: 0.8250
212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6569 - loss: 0.8253
216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6567 - loss: 0.8256
221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6565 - loss: 0.8260
226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6563 - loss: 0.8264
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6561 - loss: 0.8268
235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6560 - loss: 0.8271
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6558 - loss: 0.8274
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6557 - loss: 0.8277
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6555 - loss: 0.8280
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6554 - loss: 0.8283
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6553 - loss: 0.8286
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6551 - loss: 0.8289
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6550 - loss: 0.8291
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6549 - loss: 0.8294
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6548 - loss: 0.8296
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6546 - loss: 0.8298
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6545 - loss: 0.8301
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6543 - loss: 0.8303
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6542 - loss: 0.8305
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6541 - loss: 0.8308
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6540 - loss: 0.8310
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6538 - loss: 0.8313
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6537 - loss: 0.8315
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6536 - loss: 0.8317
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6535 - loss: 0.8319
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6535 - loss: 0.8321
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6534 - loss: 0.8323
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6533 - loss: 0.8325
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6532 - loss: 0.8326
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6532 - loss: 0.8328
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6531 - loss: 0.8329
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6531 - loss: 0.8330
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6530 - loss: 0.8332
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6530 - loss: 0.8333
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6529 - loss: 0.8334
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6529 - loss: 0.8335
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6528 - loss: 0.8336
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6528 - loss: 0.8337
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6528 - loss: 0.8338
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6527 - loss: 0.8339
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6527 - loss: 0.8340
410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6526 - loss: 0.8340
415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6526 - loss: 0.8341
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6526 - loss: 0.8342
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6525 - loss: 0.8342
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6525 - loss: 0.8342
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6525 - loss: 0.8343
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6525 - loss: 0.8343
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6524 - loss: 0.8344
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6524 - loss: 0.8344
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6524 - loss: 0.8344
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6523 - loss: 0.8344
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6523 - loss: 0.8344
Epoch 32: val_accuracy did not improve from 0.72072
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6523 - loss: 0.8345 - val_accuracy: 0.6904 - val_loss: 0.7463 - learning_rate: 0.0010
Epoch 33/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.6875 - loss: 0.6448
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6287 - loss: 0.8104
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6292 - loss: 0.8270
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6276 - loss: 0.8330
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6274 - loss: 0.8342
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6288 - loss: 0.8338
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6298 - loss: 0.8333
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6302 - loss: 0.8336
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6315 - loss: 0.8327
46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6334 - loss: 0.8314
51/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6347 - loss: 0.8313
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6353 - loss: 0.8317
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6359 - loss: 0.8320
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6363 - loss: 0.8322
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6367 - loss: 0.8321
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6369 - loss: 0.8325
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6372 - loss: 0.8328
83/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6376 - loss: 0.8328
87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6378 - loss: 0.8329
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6381 - loss: 0.8331
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6383 - loss: 0.8333
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6386 - loss: 0.8334
103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6388 - loss: 0.8336
108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6391 - loss: 0.8338
113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6394 - loss: 0.8340
117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6396 - loss: 0.8342
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6398 - loss: 0.8343
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6400 - loss: 0.8344
130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6402 - loss: 0.8344
135/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6405 - loss: 0.8344
140/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6406 - loss: 0.8344
145/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6407 - loss: 0.8345
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6408 - loss: 0.8348
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6408 - loss: 0.8351
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6409 - loss: 0.8354
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6408 - loss: 0.8357
170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6408 - loss: 0.8360
175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6407 - loss: 0.8364
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6406 - loss: 0.8367
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6405 - loss: 0.8370
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6404 - loss: 0.8372
195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6403 - loss: 0.8375
200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6403 - loss: 0.8377
205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6402 - loss: 0.8378
210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6402 - loss: 0.8379
215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6401 - loss: 0.8381
220/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6401 - loss: 0.8382
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6401 - loss: 0.8383
230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6400 - loss: 0.8385
235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6400 - loss: 0.8386
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6400 - loss: 0.8387
245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6400 - loss: 0.8388
250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6400 - loss: 0.8389
255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6400 - loss: 0.8390
260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6401 - loss: 0.8391
265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6401 - loss: 0.8391
270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6401 - loss: 0.8392
275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6402 - loss: 0.8392
280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6402 - loss: 0.8392
285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6403 - loss: 0.8392
290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6404 - loss: 0.8392
295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6404 - loss: 0.8392
299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6405 - loss: 0.8392
304/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6406 - loss: 0.8392
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6407 - loss: 0.8391
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6408 - loss: 0.8391
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6408 - loss: 0.8391
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6409 - loss: 0.8391
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6410 - loss: 0.8391
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6411 - loss: 0.8390
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6412 - loss: 0.8390
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6413 - loss: 0.8389
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6413 - loss: 0.8389
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6414 - loss: 0.8389
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6415 - loss: 0.8389
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6415 - loss: 0.8389
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6416 - loss: 0.8388
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6417 - loss: 0.8388
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6418 - loss: 0.8387
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6418 - loss: 0.8387
389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6419 - loss: 0.8386
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6419 - loss: 0.8386
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6420 - loss: 0.8386
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6420 - loss: 0.8385
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6421 - loss: 0.8385
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6421 - loss: 0.8384
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6422 - loss: 0.8384
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6422 - loss: 0.8383
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6423 - loss: 0.8383
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6423 - loss: 0.8382
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6424 - loss: 0.8381
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6424 - loss: 0.8381
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6424 - loss: 0.8380
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6425 - loss: 0.8380
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6425 - loss: 0.8379
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6426 - loss: 0.8379
Epoch 33: val_accuracy did not improve from 0.72072
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6426 - loss: 0.8378 - val_accuracy: 0.7147 - val_loss: 0.7017 - learning_rate: 0.0010
Epoch 34/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.6250 - loss: 0.8767
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.5691 - loss: 0.9941
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.5837 - loss: 0.9442
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.5961 - loss: 0.9057
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6051 - loss: 0.8858
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6109 - loss: 0.8747
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6158 - loss: 0.8676
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6186 - loss: 0.8632
42/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6213 - loss: 0.8595
47/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6229 - loss: 0.8571
52/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6243 - loss: 0.8545
57/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6258 - loss: 0.8523
62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6269 - loss: 0.8511
67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6279 - loss: 0.8500
72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6290 - loss: 0.8490
77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6299 - loss: 0.8483
82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6308 - loss: 0.8476
87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6317 - loss: 0.8466
92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6324 - loss: 0.8456
97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6332 - loss: 0.8446
102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6340 - loss: 0.8436
107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6346 - loss: 0.8427
112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6352 - loss: 0.8417
117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6357 - loss: 0.8409
122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6362 - loss: 0.8402
127/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6365 - loss: 0.8396
132/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6368 - loss: 0.8393
137/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6371 - loss: 0.8388
142/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6375 - loss: 0.8383
147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6378 - loss: 0.8378
152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6381 - loss: 0.8373
157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6384 - loss: 0.8368
162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6387 - loss: 0.8363
167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6390 - loss: 0.8359
172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6393 - loss: 0.8355
177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6395 - loss: 0.8352
182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6397 - loss: 0.8349
187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6399 - loss: 0.8346
192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6401 - loss: 0.8343
197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6403 - loss: 0.8339
202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6405 - loss: 0.8337
207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6406 - loss: 0.8334
212/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6408 - loss: 0.8331
217/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6410 - loss: 0.8329
222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6411 - loss: 0.8326
227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6412 - loss: 0.8324
232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6414 - loss: 0.8321
237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6415 - loss: 0.8318
242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6417 - loss: 0.8315
247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6418 - loss: 0.8313
252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6420 - loss: 0.8310
257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6421 - loss: 0.8308
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6422 - loss: 0.8306
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6423 - loss: 0.8305
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6424 - loss: 0.8304
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6425 - loss: 0.8303
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6425 - loss: 0.8301
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6426 - loss: 0.8300
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6427 - loss: 0.8299
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6428 - loss: 0.8297
302/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6429 - loss: 0.8296
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6430 - loss: 0.8295
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6430 - loss: 0.8295
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6431 - loss: 0.8294
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6432 - loss: 0.8293
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6433 - loss: 0.8292
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6434 - loss: 0.8291
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6434 - loss: 0.8290
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6435 - loss: 0.8290
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6436 - loss: 0.8290
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6437 - loss: 0.8290
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6437 - loss: 0.8289
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6438 - loss: 0.8289
367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6439 - loss: 0.8289
372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6439 - loss: 0.8288
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6440 - loss: 0.8288
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6441 - loss: 0.8287
387/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6442 - loss: 0.8287
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6442 - loss: 0.8286
397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6443 - loss: 0.8286
402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6444 - loss: 0.8285
407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6444 - loss: 0.8285
412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6445 - loss: 0.8284
417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6445 - loss: 0.8284
422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6446 - loss: 0.8284
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6446 - loss: 0.8284
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6446 - loss: 0.8284
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6447 - loss: 0.8284
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6447 - loss: 0.8284
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6447 - loss: 0.8284
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6447 - loss: 0.8284
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6448 - loss: 0.8284
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6448 - loss: 0.8284
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6448 - loss: 0.8285
Epoch 34: val_accuracy did not improve from 0.72072
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6448 - loss: 0.8285 - val_accuracy: 0.7193 - val_loss: 0.6982 - learning_rate: 0.0010
Epoch 35/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.5938 - loss: 0.9907
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6404 - loss: 0.8769
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6631 - loss: 0.8377
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6712 - loss: 0.8170
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6741 - loss: 0.8077
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6739 - loss: 0.8057
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6734 - loss: 0.8051
36/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6738 - loss: 0.8032
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6741 - loss: 0.8022
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6739 - loss: 0.8017
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6731 - loss: 0.8019
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6722 - loss: 0.8025
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6715 - loss: 0.8027
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6707 - loss: 0.8030
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6698 - loss: 0.8034
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6692 - loss: 0.8036
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6687 - loss: 0.8035
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6683 - loss: 0.8035
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6680 - loss: 0.8035
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6677 - loss: 0.8036
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6675 - loss: 0.8034
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6674 - loss: 0.8032
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6673 - loss: 0.8032
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6670 - loss: 0.8035
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6668 - loss: 0.8037
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6666 - loss: 0.8040
129/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6663 - loss: 0.8044
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6660 - loss: 0.8049
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6657 - loss: 0.8053
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6656 - loss: 0.8057
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6655 - loss: 0.8059
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6654 - loss: 0.8060
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6653 - loss: 0.8062
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6651 - loss: 0.8064
168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6650 - loss: 0.8065
173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6648 - loss: 0.8067
178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6646 - loss: 0.8069
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6644 - loss: 0.8070
188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6642 - loss: 0.8072
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6640 - loss: 0.8074
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6638 - loss: 0.8076
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6636 - loss: 0.8078
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6634 - loss: 0.8080
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6632 - loss: 0.8083
218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6630 - loss: 0.8086
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6628 - loss: 0.8088
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6627 - loss: 0.8090
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6625 - loss: 0.8092
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6624 - loss: 0.8094
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6622 - loss: 0.8096
247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6621 - loss: 0.8097
252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6619 - loss: 0.8099
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6618 - loss: 0.8100
261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6617 - loss: 0.8103
266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6615 - loss: 0.8105
270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6614 - loss: 0.8107
275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6612 - loss: 0.8109
280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6611 - loss: 0.8111
285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6610 - loss: 0.8114
290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6608 - loss: 0.8117
295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6607 - loss: 0.8119
300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6605 - loss: 0.8122
305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6604 - loss: 0.8124
310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6602 - loss: 0.8127
315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6601 - loss: 0.8129
320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6599 - loss: 0.8131
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6598 - loss: 0.8134
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6596 - loss: 0.8136
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6595 - loss: 0.8138
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6594 - loss: 0.8140
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6593 - loss: 0.8141
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6592 - loss: 0.8142
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6592 - loss: 0.8144
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6591 - loss: 0.8145
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6590 - loss: 0.8146
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6590 - loss: 0.8147
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6589 - loss: 0.8148
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6588 - loss: 0.8149
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6588 - loss: 0.8150
390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6587 - loss: 0.8151
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6586 - loss: 0.8152
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6586 - loss: 0.8153
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6585 - loss: 0.8154
410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6585 - loss: 0.8156
415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6584 - loss: 0.8157
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6583 - loss: 0.8158
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Epoch 35: val_accuracy improved from 0.72072 to 0.72493, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6576 - loss: 0.8174 - val_accuracy: 0.7249 - val_loss: 0.6951 - learning_rate: 0.0010
Epoch 36/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.6875 - loss: 0.9339
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6793 - loss: 0.8543
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6624 - loss: 0.8614
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6611 - loss: 0.8578
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6600 - loss: 0.8561
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6578 - loss: 0.8563
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55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6549 - loss: 0.8444
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135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6513 - loss: 0.8336
140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6511 - loss: 0.8335
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215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6513 - loss: 0.8309
220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6514 - loss: 0.8308
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6514 - loss: 0.8307
230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6514 - loss: 0.8306
235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6514 - loss: 0.8305
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6514 - loss: 0.8303
245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6514 - loss: 0.8302
250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6515 - loss: 0.8301
255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6515 - loss: 0.8301
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270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6516 - loss: 0.8298
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280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6517 - loss: 0.8297
285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6517 - loss: 0.8296
290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6518 - loss: 0.8296
295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6518 - loss: 0.8295
300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6518 - loss: 0.8295
304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6518 - loss: 0.8295
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6518 - loss: 0.8294
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6519 - loss: 0.8294
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6519 - loss: 0.8293
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6519 - loss: 0.8293
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6520 - loss: 0.8292
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6520 - loss: 0.8292
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6520 - loss: 0.8291
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6521 - loss: 0.8291
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6521 - loss: 0.8291
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6521 - loss: 0.8290
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6521 - loss: 0.8290
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6522 - loss: 0.8289
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6522 - loss: 0.8289
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384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6523 - loss: 0.8288
389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6523 - loss: 0.8287
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399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6524 - loss: 0.8286
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6524 - loss: 0.8286
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414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6524 - loss: 0.8285
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6525 - loss: 0.8284
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429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6525 - loss: 0.8284
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6525 - loss: 0.8283
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6525 - loss: 0.8283
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6526 - loss: 0.8283
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6526 - loss: 0.8282
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6526 - loss: 0.8282
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6527 - loss: 0.8282
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6527 - loss: 0.8281
Epoch 36: val_accuracy did not improve from 0.72493
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6528 - loss: 0.8280 - val_accuracy: 0.7127 - val_loss: 0.7183 - learning_rate: 0.0010
Epoch 37/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 108ms/step - accuracy: 0.5938 - loss: 0.8450
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5813 - loss: 0.8829
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5900 - loss: 0.8712
14/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.5973 - loss: 0.8594
19/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6081 - loss: 0.8468
23/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6158 - loss: 0.8404
28/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6249 - loss: 0.8337
32/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6305 - loss: 0.8301
37/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6353 - loss: 0.8271
42/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6382 - loss: 0.8258
47/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6405 - loss: 0.8244
52/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6422 - loss: 0.8234
56/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6432 - loss: 0.8228
61/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6441 - loss: 0.8225
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Epoch 37: val_accuracy improved from 0.72493 to 0.72654, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6509 - loss: 0.8302 - val_accuracy: 0.7265 - val_loss: 0.6899 - learning_rate: 0.0010
Epoch 38/45
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Epoch 38: val_accuracy improved from 0.72654 to 0.72835, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6485 - loss: 0.8152 - val_accuracy: 0.7284 - val_loss: 0.6847 - learning_rate: 0.0010
Epoch 39/45
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315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6665 - loss: 0.8048
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6665 - loss: 0.8048
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6664 - loss: 0.8049
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6664 - loss: 0.8050
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6663 - loss: 0.8050
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6663 - loss: 0.8051
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6662 - loss: 0.8051
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6661 - loss: 0.8052
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6661 - loss: 0.8052
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6661 - loss: 0.8053
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6661 - loss: 0.8053
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6660 - loss: 0.8053
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6660 - loss: 0.8053
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6660 - loss: 0.8053
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6660 - loss: 0.8053
388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6659 - loss: 0.8053
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6659 - loss: 0.8052
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6659 - loss: 0.8052
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6659 - loss: 0.8052
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6658 - loss: 0.8053
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6658 - loss: 0.8053
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6658 - loss: 0.8053
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6658 - loss: 0.8053
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6657 - loss: 0.8053
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6657 - loss: 0.8053
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6657 - loss: 0.8053
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6657 - loss: 0.8053
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6656 - loss: 0.8053
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6656 - loss: 0.8054
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6656 - loss: 0.8054
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6655 - loss: 0.8054
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6655 - loss: 0.8054
Epoch 39: val_accuracy did not improve from 0.72835
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6654 - loss: 0.8055 - val_accuracy: 0.7121 - val_loss: 0.6972 - learning_rate: 0.0010
Epoch 40/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 46s 99ms/step - accuracy: 0.7188 - loss: 0.8825
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6578 - loss: 0.8561
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6516 - loss: 0.8394
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6577 - loss: 0.8269
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6595 - loss: 0.8211
24/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6588 - loss: 0.8213
29/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6577 - loss: 0.8202
34/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6569 - loss: 0.8177
39/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6555 - loss: 0.8164
44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6548 - loss: 0.8146
49/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6546 - loss: 0.8133
54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6544 - loss: 0.8124
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6541 - loss: 0.8121
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6539 - loss: 0.8112
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6537 - loss: 0.8108
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6536 - loss: 0.8102
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6537 - loss: 0.8095
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6538 - loss: 0.8088
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6539 - loss: 0.8082
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6540 - loss: 0.8075
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6541 - loss: 0.8068
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6544 - loss: 0.8060
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6547 - loss: 0.8053
113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6550 - loss: 0.8045
118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6554 - loss: 0.8037
123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6555 - loss: 0.8032
128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6556 - loss: 0.8028
133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6557 - loss: 0.8024
138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6558 - loss: 0.8021
143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6560 - loss: 0.8019
148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6561 - loss: 0.8017
153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6562 - loss: 0.8014
157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6563 - loss: 0.8012
162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6564 - loss: 0.8011
167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6564 - loss: 0.8009
172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6565 - loss: 0.8009
177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6565 - loss: 0.8009
182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6565 - loss: 0.8010
187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6565 - loss: 0.8010
192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6565 - loss: 0.8011
197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6566 - loss: 0.8011
202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6566 - loss: 0.8012
207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6566 - loss: 0.8013
212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6566 - loss: 0.8014
217/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6567 - loss: 0.8014
222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6567 - loss: 0.8014
227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6568 - loss: 0.8015
232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6568 - loss: 0.8015
237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6569 - loss: 0.8016
242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6569 - loss: 0.8016
247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6569 - loss: 0.8017
252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6570 - loss: 0.8018
257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6570 - loss: 0.8019
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6570 - loss: 0.8019
266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6571 - loss: 0.8019
271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6571 - loss: 0.8020
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6572 - loss: 0.8020
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6573 - loss: 0.8020
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6573 - loss: 0.8020
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6574 - loss: 0.8020
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6574 - loss: 0.8021
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6575 - loss: 0.8022
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8023
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8024
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8025
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8027
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8028
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8029
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8030
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8032
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8033
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8034
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6576 - loss: 0.8035
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6576 - loss: 0.8036
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6576 - loss: 0.8037
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6577 - loss: 0.8038
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6577 - loss: 0.8038
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6578 - loss: 0.8039
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6578 - loss: 0.8040
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6579 - loss: 0.8040
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6579 - loss: 0.8040
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6580 - loss: 0.8041
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6580 - loss: 0.8042
411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6581 - loss: 0.8042
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6581 - loss: 0.8043
421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6582 - loss: 0.8043
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6582 - loss: 0.8044
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6582 - loss: 0.8044
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6583 - loss: 0.8045
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6583 - loss: 0.8045
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6583 - loss: 0.8046
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6583 - loss: 0.8046
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6584 - loss: 0.8047
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6584 - loss: 0.8048
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Epoch 40: val_accuracy improved from 0.72835 to 0.73076, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6584 - loss: 0.8050 - val_accuracy: 0.7308 - val_loss: 0.6777 - learning_rate: 0.0010
Epoch 41/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.7188 - loss: 0.7281
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6654 - loss: 0.7672
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6650 - loss: 0.7752
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6656 - loss: 0.7704
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6614 - loss: 0.7760
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6600 - loss: 0.7785
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71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6613 - loss: 0.7830
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230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6635 - loss: 0.7905
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305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6634 - loss: 0.7931
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315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6634 - loss: 0.7934
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324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6634 - loss: 0.7937
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6634 - loss: 0.7939
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6634 - loss: 0.7940
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364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6633 - loss: 0.7946
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432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6632 - loss: 0.7962
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6632 - loss: 0.7963
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6632 - loss: 0.7964
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6632 - loss: 0.7965
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6632 - loss: 0.7966
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6632 - loss: 0.7966
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6632 - loss: 0.7967
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6632 - loss: 0.7968
Epoch 41: val_accuracy did not improve from 0.73076
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6632 - loss: 0.7969 - val_accuracy: 0.7298 - val_loss: 0.6751 - learning_rate: 0.0010
Epoch 42/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 107ms/step - accuracy: 0.5625 - loss: 0.7943
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6110 - loss: 0.8452
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6324 - loss: 0.8380
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6456 - loss: 0.8233
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6524 - loss: 0.8151
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6552 - loss: 0.8121
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35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6546 - loss: 0.8148
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55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6568 - loss: 0.8129
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6573 - loss: 0.8119
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70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6585 - loss: 0.8096
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6589 - loss: 0.8088
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85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6594 - loss: 0.8075
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Epoch 42: val_accuracy improved from 0.73076 to 0.73398, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6653 - loss: 0.8023 - val_accuracy: 0.7340 - val_loss: 0.6685 - learning_rate: 0.0010
Epoch 43/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.5625 - loss: 0.9353
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209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6653 - loss: 0.7939
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217/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6655 - loss: 0.7939
222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6655 - loss: 0.7939
227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6656 - loss: 0.7939
232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6657 - loss: 0.7939
237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6658 - loss: 0.7939
242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6659 - loss: 0.7939
247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6660 - loss: 0.7939
252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6661 - loss: 0.7939
257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6661 - loss: 0.7939
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7940
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7940
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7941
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7942
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7943
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7944
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6661 - loss: 0.7944
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6661 - loss: 0.7946
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6661 - loss: 0.7947
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6660 - loss: 0.7948
310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6660 - loss: 0.7949
313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6659 - loss: 0.7949
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6659 - loss: 0.7950
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6659 - loss: 0.7951
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6659 - loss: 0.7952
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6658 - loss: 0.7952
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6658 - loss: 0.7953
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6658 - loss: 0.7954
343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6658 - loss: 0.7954
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6658 - loss: 0.7955
353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6657 - loss: 0.7956
358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6657 - loss: 0.7957
363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6657 - loss: 0.7958
368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6656 - loss: 0.7958
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6656 - loss: 0.7959
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6656 - loss: 0.7959
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6656 - loss: 0.7960
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6655 - loss: 0.7961
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6655 - loss: 0.7961
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6655 - loss: 0.7962
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6655 - loss: 0.7962
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6654 - loss: 0.7963
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6654 - loss: 0.7963
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6654 - loss: 0.7964
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6654 - loss: 0.7964
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6653 - loss: 0.7965
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6653 - loss: 0.7965
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6653 - loss: 0.7966
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6652 - loss: 0.7967
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6652 - loss: 0.7967
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6652 - loss: 0.7968
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6652 - loss: 0.7968
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6652 - loss: 0.7969
Epoch 43: val_accuracy did not improve from 0.73398
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6651 - loss: 0.7971 - val_accuracy: 0.7205 - val_loss: 0.7075 - learning_rate: 0.0010
Epoch 44/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.5625 - loss: 0.7509
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6342 - loss: 0.7526
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6516 - loss: 0.7573
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6455 - loss: 0.7804
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6449 - loss: 0.7869
24/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6462 - loss: 0.7888
28/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6488 - loss: 0.7882
32/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6515 - loss: 0.7868
35/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.6536 - loss: 0.7853
39/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.6565 - loss: 0.7828
44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6593 - loss: 0.7799
49/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6610 - loss: 0.7782
54/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6623 - loss: 0.7775
59/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6634 - loss: 0.7769
64/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6643 - loss: 0.7761
69/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6654 - loss: 0.7752
74/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6662 - loss: 0.7745
79/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6668 - loss: 0.7741
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6672 - loss: 0.7737
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6676 - loss: 0.7736
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6679 - loss: 0.7737
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6681 - loss: 0.7738
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6681 - loss: 0.7740
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6681 - loss: 0.7744
113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6682 - loss: 0.7746
118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6682 - loss: 0.7748
123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6682 - loss: 0.7751
128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6681 - loss: 0.7755
133/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6681 - loss: 0.7759
138/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6680 - loss: 0.7762
143/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6680 - loss: 0.7766
148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6679 - loss: 0.7769
153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6678 - loss: 0.7772
158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6678 - loss: 0.7776
163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6677 - loss: 0.7779
168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6676 - loss: 0.7781
173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6676 - loss: 0.7784
178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6675 - loss: 0.7786
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6675 - loss: 0.7788
188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6674 - loss: 0.7791
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6673 - loss: 0.7794
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6672 - loss: 0.7796
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6671 - loss: 0.7799
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6670 - loss: 0.7802
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6669 - loss: 0.7805
218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6668 - loss: 0.7807
223/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6668 - loss: 0.7809
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6667 - loss: 0.7811
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6667 - loss: 0.7813
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6666 - loss: 0.7815
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6666 - loss: 0.7817
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6666 - loss: 0.7819
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7821
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7823
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7825
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7827
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6664 - loss: 0.7829
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6664 - loss: 0.7830
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7832
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7833
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7834
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7835
303/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6666 - loss: 0.7836
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7837
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7838
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7839
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7840
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7841
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7843
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7844
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7846
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7847
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7849
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7851
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7853
367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7855
372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7857
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7859
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6664 - loss: 0.7861
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6664 - loss: 0.7863
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6664 - loss: 0.7865
397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6664 - loss: 0.7867
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6664 - loss: 0.7868
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7869
410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7871
415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7873
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7874
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7876
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7877
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7879
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.7880
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.7882
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.7883
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.7884
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.7885
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.7886
Epoch 44: val_accuracy did not improve from 0.73398
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6662 - loss: 0.7888 - val_accuracy: 0.7304 - val_loss: 0.6849 - learning_rate: 0.0010
Epoch 45/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.5312 - loss: 0.9538
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6024 - loss: 0.8991
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6321 - loss: 0.8606
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6435 - loss: 0.8421
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6508 - loss: 0.8284
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6562 - loss: 0.8174
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6599 - loss: 0.8101
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6619 - loss: 0.8050
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6638 - loss: 0.8012
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6648 - loss: 0.7987
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6654 - loss: 0.7971
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6657 - loss: 0.7958
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6656 - loss: 0.7952
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6655 - loss: 0.7947
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6654 - loss: 0.7940
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6653 - loss: 0.7935
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6651 - loss: 0.7933
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6649 - loss: 0.7934
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6647 - loss: 0.7935
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6645 - loss: 0.7936
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6644 - loss: 0.7937
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6642 - loss: 0.7938
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6641 - loss: 0.7940
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6641 - loss: 0.7941
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6641 - loss: 0.7940
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6642 - loss: 0.7940
130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6642 - loss: 0.7940
135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6643 - loss: 0.7939
140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6643 - loss: 0.7940
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6643 - loss: 0.7941
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6643 - loss: 0.7941
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6644 - loss: 0.7942
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6645 - loss: 0.7941
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6646 - loss: 0.7940
168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6647 - loss: 0.7940
173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6648 - loss: 0.7939
178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6650 - loss: 0.7939
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6651 - loss: 0.7939
188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6652 - loss: 0.7938
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6654 - loss: 0.7937
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6655 - loss: 0.7936
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6656 - loss: 0.7935
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6657 - loss: 0.7934
214/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6657 - loss: 0.7933
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6658 - loss: 0.7932
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6659 - loss: 0.7931
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6659 - loss: 0.7930
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6660 - loss: 0.7929
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6661 - loss: 0.7928
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7927
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7926
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6663 - loss: 0.7925
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6663 - loss: 0.7925
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6663 - loss: 0.7924
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6663 - loss: 0.7924
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6663 - loss: 0.7923
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6663 - loss: 0.7923
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6664 - loss: 0.7922
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6664 - loss: 0.7922
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6664 - loss: 0.7921
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7920
302/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7919
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7919
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7918
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7917
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7916
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7915
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7915
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.7914
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.7914
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.7913
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.7913
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.7913
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.7913
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6668 - loss: 0.7913
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6668 - loss: 0.7913
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6668 - loss: 0.7912
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6668 - loss: 0.7912
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6668 - loss: 0.7912
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6668 - loss: 0.7912
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6668 - loss: 0.7912
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6669 - loss: 0.7912
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6669 - loss: 0.7912
410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6669 - loss: 0.7912
415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6669 - loss: 0.7912
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6669 - loss: 0.7911
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6669 - loss: 0.7911
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6669 - loss: 0.7912
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6670 - loss: 0.7912
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6670 - loss: 0.7911
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6670 - loss: 0.7911
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6670 - loss: 0.7911
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6670 - loss: 0.7912
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6670 - loss: 0.7912
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6671 - loss: 0.7912
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6671 - loss: 0.7912
Epoch 45: val_accuracy did not improve from 0.73398
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6671 - loss: 0.7912 - val_accuracy: 0.7284 - val_loss: 0.6867 - learning_rate: 0.0010
Restoring model weights from the end of the best epoch: 42.
Plotting the Training and Validation Accuracies¶
plt.plot(history_1.history["accuracy"])
plt.plot(history_1.history["val_accuracy"])
plt.title("CNN Model 1 accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train", "validation"], loc="upper left")
plt.show()
Evaluating the Model on the Test Set¶
# Calculate the number of steps for the entire test set to be processed
test_steps = test_generator.samples // batch_size
# If the number of samples isn't a multiple of the batch size,
# you have one more batch with the remaining samples
if test_generator.samples % batch_size > 0:
test_steps += 1
# Evaluating the model on the test set
evaluation_results = model_1.evaluate(test_generator, steps=test_steps)
print(f"Loss: {evaluation_results[0]}, Accuracy: {evaluation_results[1]}")
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.8438 - loss: 0.4053
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7573 - loss: 0.5913
Loss: 0.65003901720047, Accuracy: 0.734375
Plotting Confusion Matrix¶
pred_probabilities = model_1.predict(test_generator, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)
# Getting the true labels from the generator
y_true = test_generator.classes
# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))
# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("CNN Model 1 Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 1s 365ms/step
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step
precision recall f1-score support
happy 0.84 0.84 0.84 32
neutral 0.65 0.69 0.67 32
sad 0.54 0.59 0.57 32
surprise 0.96 0.81 0.88 32
accuracy 0.73 128
macro avg 0.75 0.73 0.74 128
weighted avg 0.75 0.73 0.74 128
Observations and Insights:
- The model has a substantial number of 651,780 trainable parameters, suggesting a complex architecture capable of learning detailed features.
- The model's performance on the test set shows an accuracy of 73.44%.
- Regarding the confusion matrix, the model identifies the 'surprise' emotion with an f1-score of 0.88, and performs also well with 'happy' at an f1-score of 0.84. However, 'neutral' and 'sad' emotions have lower f1-scores of 0.67 and 0.57 respectively, suggesting the model's difficulty in distinguishing these emotions as accurately.
- The f1-score, which balances precision and recall, suggests that 'sad' is the most challenging emotion for the model to predict correctly.
Creating the second Convolutional Neural Network¶
Model 2 Architecture:¶
This model is designed with a sequential structure, incorporating four convolutional blocks for feature extraction, followed by dense layers for classification.
First Convolutional Block:
- Begins with a Conv2D layer with 256 filters, a 2x2 kernel size, 'same' padding, and an input shape of (48, 48, 1), indicating grayscale images of size 48x48.
- Includes BatchNormalization to stabilize and speed up training.
- Utilizes LeakyReLU with a negative slope of 0.1 for activation, allowing a small gradient when the unit is not active.
- Applies MaxPooling2D with a pool size of 2 to reduce spatial dimensions.
Second Convolutional Block:
- Consists of a Conv2D layer with 128 filters and a 2x2 kernel size, using 'same' padding.
- Follows the same pattern of BatchNormalization, LeakyReLU activation, and MaxPooling2D.
Third Convolutional Block:
- Features a Conv2D layer with 64 filters and a 2x2 kernel size, maintaining 'same' padding.
- Repeats the BatchNormalization, LeakyReLU activation, and MaxPooling2D structure.
Fourth Convolutional Block:
- Contains a Conv2D layer with 32 filters and a 2x2 kernel size, with 'same' padding.
- Continues with BatchNormalization, LeakyReLU activation, and MaxPooling2D.
After processing through the convolutional blocks, the model flattens the output to transition to fully connected layers.
Fully Connected Dense Layers:
- Incorporates a dense layer with 512 neurons and 'relu' activation.
- Followed by a dense layer with 128 neurons and 'relu' activation.
- Then, a dense layer with 64 neurons without an explicit activation is added before BatchNormalization and ReLU activation to introduce non-linearity.
Output Layer:
- Concludes with a Dense layer having 4 neurons and 'softmax' activation for multi-class classification of 4 emotions.
The model employs the Adam optimizer with a learning rate of 0.001, optimizing the categorical crossentropy loss function for training.
backend.clear_session()
# Fixing the seed for random number generators so that we can ensure we receive the same output everytime
np.random.seed(42)
random.seed(42)
tf.random.set_seed(42)
# Initializing a sequential model
model_2 = Sequential()
model_2.add(Input(shape=(img_width, img_height, color_layers)))
# First Convolutional Block
model_2.add(Conv2D(256, kernel_size=2, padding="same"))
model_2.add(BatchNormalization())
model_2.add(LeakyReLU(negative_slope=0.1))
model_2.add(MaxPooling2D(pool_size=2))
# Second Convolutional Block
model_2.add(Conv2D(128, kernel_size=2, padding="same"))
model_2.add(BatchNormalization())
model_2.add(LeakyReLU(negative_slope=0.1))
model_2.add(MaxPooling2D(pool_size=2))
# Third Convolutional Block
model_2.add(Conv2D(64, kernel_size=2, padding="same"))
model_2.add(BatchNormalization())
model_2.add(LeakyReLU(negative_slope=0.1))
model_2.add(MaxPooling2D(pool_size=2))
# Fourth Convolutional Block
model_2.add(Conv2D(32, kernel_size=2, padding="same"))
model_2.add(BatchNormalization())
model_2.add(LeakyReLU(negative_slope=0.1))
model_2.add(MaxPooling2D(pool_size=2))
# Flatten the output of the conv layers to feed into the dense layers
model_2.add(Flatten())
# Fully connected layers
model_2.add(Dense(512, activation="relu"))
model_2.add(Dense(128, activation="relu"))
model_2.add(Dense(64))
model_2.add(BatchNormalization())
model_2.add(ReLU()) # Using ReLU after batch normalization
# Adding output layer
model_2.add(Dense(4, activation="softmax"))
# Using Adam Optimizer
optimizer = Adam(learning_rate=0.001)
Compiling and Training the Model¶
# Compile the model
model_2.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])
model_2.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ conv2d (Conv2D) │ (None, 48, 48, 256) │ 1,280 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization │ (None, 48, 48, 256) │ 1,024 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ leaky_re_lu (LeakyReLU) │ (None, 48, 48, 256) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d (MaxPooling2D) │ (None, 24, 24, 256) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_1 (Conv2D) │ (None, 24, 24, 128) │ 131,200 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization_1 │ (None, 24, 24, 128) │ 512 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ leaky_re_lu_1 (LeakyReLU) │ (None, 24, 24, 128) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_1 (MaxPooling2D) │ (None, 12, 12, 128) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_2 (Conv2D) │ (None, 12, 12, 64) │ 32,832 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization_2 │ (None, 12, 12, 64) │ 256 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ leaky_re_lu_2 (LeakyReLU) │ (None, 12, 12, 64) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_2 (MaxPooling2D) │ (None, 6, 6, 64) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_3 (Conv2D) │ (None, 6, 6, 32) │ 8,224 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization_3 │ (None, 6, 6, 32) │ 128 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ leaky_re_lu_3 (LeakyReLU) │ (None, 6, 6, 32) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_3 (MaxPooling2D) │ (None, 3, 3, 32) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ flatten (Flatten) │ (None, 288) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense (Dense) │ (None, 512) │ 147,968 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_1 (Dense) │ (None, 128) │ 65,664 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_2 (Dense) │ (None, 64) │ 8,256 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization_4 │ (None, 64) │ 256 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ re_lu (ReLU) │ (None, 64) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_3 (Dense) │ (None, 4) │ 260 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 397,860 (1.52 MB)
Trainable params: 396,772 (1.51 MB)
Non-trainable params: 1,088 (4.25 KB)
# Get the current time
current_time = datetime.now().strftime("%Y%m%d-%H%M%S")
# Set up Early Stopping with a patience 7 but acting after at least 30 epochs
delayed_early_stopping = DelayedEarlyStopping(
monitor="val_loss", patience=7, verbose=1, restore_best_weights=True, start_epoch=30
)
# Define the learning rate scheduler callback
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, patience=5, min_lr=0.00001, verbose=1)
# Define the saving the best model callback
mc = ModelCheckpoint(
f"{results_path}/best_model_2_{current_time}.keras",
monitor="val_accuracy",
mode="max",
verbose=1,
save_best_only=True,
)
# Fitting the model with 45 epochs and using validation set
history_2 = model_2.fit(
train_generator,
epochs=45,
validation_data=validation_generator,
callbacks=[reduce_lr, mc, delayed_early_stopping],
)
Epoch 1/45
I0000 00:00:1712794150.228648 1482664 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_2631', 4 bytes spill stores, 4 bytes spill loads
1/473 ━━━━━━━━━━━━━━━━━━━━ 42:28 5s/step - accuracy: 0.3438 - loss: 1.3840
5/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.2798 - loss: 1.6040
8/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.2899 - loss: 1.6063
13/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.2964 - loss: 1.6224
18/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.2983 - loss: 1.6201
22/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.2994 - loss: 1.6173
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50/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.3097 - loss: 1.5655
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85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.3187 - loss: 1.5175
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101/473 ━━━━━━━━━━━━━━━━━━━━ 15s 42ms/step - accuracy: 0.3215 - loss: 1.5019
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110/473 ━━━━━━━━━━━━━━━━━━━━ 14s 40ms/step - accuracy: 0.3229 - loss: 1.4944
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Epoch 1: val_accuracy improved from -inf to 0.38798, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 17s 24ms/step - accuracy: 0.3616 - loss: 1.3675 - val_accuracy: 0.3880 - val_loss: 1.2428 - learning_rate: 0.0010
Epoch 2/45
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Epoch 2: val_accuracy improved from 0.38798 to 0.47197, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.4843 - loss: 1.1404 - val_accuracy: 0.4720 - val_loss: 1.3282 - learning_rate: 0.0010
Epoch 3/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 106ms/step - accuracy: 0.5312 - loss: 1.1768
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232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5576 - loss: 1.0405
237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5578 - loss: 1.0401
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319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5597 - loss: 1.0339
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5598 - loss: 1.0336
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5599 - loss: 1.0333
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344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5601 - loss: 1.0324
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437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5614 - loss: 1.0272
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5615 - loss: 1.0269
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5615 - loss: 1.0267
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5616 - loss: 1.0264
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5617 - loss: 1.0262
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5618 - loss: 1.0259
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Epoch 3: val_accuracy improved from 0.47197 to 0.52863, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5619 - loss: 1.0254 - val_accuracy: 0.5286 - val_loss: 1.1775 - learning_rate: 0.0010
Epoch 4/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 59s 127ms/step - accuracy: 0.5938 - loss: 1.0203
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5871 - loss: 0.9692
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5939 - loss: 0.9558
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5970 - loss: 0.9501
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5945 - loss: 0.9543
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Epoch 4: val_accuracy improved from 0.52863 to 0.65702, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5950 - loss: 0.9466 - val_accuracy: 0.6570 - val_loss: 0.8246 - learning_rate: 0.0010
Epoch 5/45
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6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5618 - loss: 0.9273
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156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6028 - loss: 0.9184
161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6030 - loss: 0.9180
166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6032 - loss: 0.9176
171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6035 - loss: 0.9172
176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6037 - loss: 0.9169
181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6039 - loss: 0.9166
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6041 - loss: 0.9164
191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6043 - loss: 0.9162
196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6045 - loss: 0.9160
201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6047 - loss: 0.9158
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6049 - loss: 0.9156
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6051 - loss: 0.9155
216/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6052 - loss: 0.9155
221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6053 - loss: 0.9154
226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6054 - loss: 0.9155
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6054 - loss: 0.9155
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6055 - loss: 0.9155
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6056 - loss: 0.9155
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6056 - loss: 0.9155
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6057 - loss: 0.9154
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6058 - loss: 0.9154
261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6059 - loss: 0.9153
266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6060 - loss: 0.9152
271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6061 - loss: 0.9151
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6062 - loss: 0.9150
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6062 - loss: 0.9149
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6063 - loss: 0.9148
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6063 - loss: 0.9147
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6064 - loss: 0.9147
301/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6064 - loss: 0.9146
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6065 - loss: 0.9146
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6066 - loss: 0.9145
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6066 - loss: 0.9144
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6066 - loss: 0.9143
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6067 - loss: 0.9143
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6067 - loss: 0.9142
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6067 - loss: 0.9142
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6068 - loss: 0.9141
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6068 - loss: 0.9141
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6068 - loss: 0.9140
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6069 - loss: 0.9140
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6069 - loss: 0.9139
367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6070 - loss: 0.9138
372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6070 - loss: 0.9137
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6071 - loss: 0.9136
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6072 - loss: 0.9134
387/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6072 - loss: 0.9133
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6073 - loss: 0.9132
397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6074 - loss: 0.9131
402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6074 - loss: 0.9130
407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6075 - loss: 0.9129
412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6075 - loss: 0.9128
417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6076 - loss: 0.9127
422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6076 - loss: 0.9127
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6077 - loss: 0.9126
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6077 - loss: 0.9125
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6078 - loss: 0.9125
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6078 - loss: 0.9124
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6079 - loss: 0.9123
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6079 - loss: 0.9122
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6080 - loss: 0.9121
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6081 - loss: 0.9120
467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6081 - loss: 0.9119
Epoch 5: val_accuracy did not improve from 0.65702
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6082 - loss: 0.9118 - val_accuracy: 0.6319 - val_loss: 0.8880 - learning_rate: 0.0010
Epoch 6/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 51s 108ms/step - accuracy: 0.5938 - loss: 0.8551
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.5984 - loss: 0.8713
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6135 - loss: 0.8580
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6202 - loss: 0.8558
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6244 - loss: 0.8547
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6284 - loss: 0.8522
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6290 - loss: 0.8541
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6283 - loss: 0.8576
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6276 - loss: 0.8608
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6266 - loss: 0.8639
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6259 - loss: 0.8659
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6259 - loss: 0.8669
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6255 - loss: 0.8685
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6252 - loss: 0.8700
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6251 - loss: 0.8712
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6253 - loss: 0.8717
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6256 - loss: 0.8719
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6259 - loss: 0.8719
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6263 - loss: 0.8718
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6267 - loss: 0.8717
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6269 - loss: 0.8716
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6271 - loss: 0.8716
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6273 - loss: 0.8716
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6274 - loss: 0.8716
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6275 - loss: 0.8717
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6276 - loss: 0.8718
130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6276 - loss: 0.8717
135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6276 - loss: 0.8717
140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6276 - loss: 0.8719
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6277 - loss: 0.8719
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8720
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8720
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8722
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8723
170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6279 - loss: 0.8725
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6279 - loss: 0.8727
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8730
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8732
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8733
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8735
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8736
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8737
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8737
214/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6278 - loss: 0.8737
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6278 - loss: 0.8736
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6279 - loss: 0.8736
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6279 - loss: 0.8736
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6279 - loss: 0.8736
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6278 - loss: 0.8736
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6278 - loss: 0.8736
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6278 - loss: 0.8737
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6278 - loss: 0.8737
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6278 - loss: 0.8736
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6278 - loss: 0.8736
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6278 - loss: 0.8736
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6278 - loss: 0.8735
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6278 - loss: 0.8734
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6278 - loss: 0.8734
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6279 - loss: 0.8733
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6279 - loss: 0.8732
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6279 - loss: 0.8732
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Epoch 6: val_accuracy improved from 0.65702 to 0.66024, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6291 - loss: 0.8715 - val_accuracy: 0.6602 - val_loss: 0.8272 - learning_rate: 0.0010
Epoch 7/45
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465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6362 - loss: 0.8523
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Epoch 7: val_accuracy improved from 0.66024 to 0.68957, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6362 - loss: 0.8523 - val_accuracy: 0.6896 - val_loss: 0.7543 - learning_rate: 0.0010
Epoch 8/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 106ms/step - accuracy: 0.9062 - loss: 0.4684
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7533 - loss: 0.7041
10/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7315 - loss: 0.7319
14/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7162 - loss: 0.7507
19/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7059 - loss: 0.7625
24/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6979 - loss: 0.7704
29/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6925 - loss: 0.7749
34/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6884 - loss: 0.7782
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43/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6818 - loss: 0.7851
48/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6788 - loss: 0.7880
53/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6767 - loss: 0.7898
58/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6747 - loss: 0.7916
64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6724 - loss: 0.7932
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6707 - loss: 0.7945
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93/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6653 - loss: 0.7996
97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6646 - loss: 0.8005
102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6638 - loss: 0.8014
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136/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6596 - loss: 0.8082
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175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6564 - loss: 0.8142
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184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6560 - loss: 0.8151
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193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6555 - loss: 0.8160
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6553 - loss: 0.8165
202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6551 - loss: 0.8169
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6549 - loss: 0.8173
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6547 - loss: 0.8177
216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6544 - loss: 0.8182
220/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6542 - loss: 0.8186
225/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6539 - loss: 0.8191
230/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6536 - loss: 0.8195
235/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6534 - loss: 0.8199
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249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6527 - loss: 0.8212
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6525 - loss: 0.8216
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6523 - loss: 0.8221
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6520 - loss: 0.8225
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6519 - loss: 0.8228
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6517 - loss: 0.8233
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6515 - loss: 0.8236
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6513 - loss: 0.8240
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6511 - loss: 0.8244
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6509 - loss: 0.8248
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6507 - loss: 0.8252
300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6506 - loss: 0.8255
304/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6504 - loss: 0.8258
309/473 ━━━━━━━━━━━━━━━━━━━━ 2s 13ms/step - accuracy: 0.6502 - loss: 0.8262
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.6500 - loss: 0.8265
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.6499 - loss: 0.8268
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.6498 - loss: 0.8271
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.6497 - loss: 0.8274
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6496 - loss: 0.8276
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6494 - loss: 0.8279
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6493 - loss: 0.8281
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6492 - loss: 0.8283
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6491 - loss: 0.8286
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6490 - loss: 0.8288
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6489 - loss: 0.8290
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6488 - loss: 0.8292
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.6487 - loss: 0.8294
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6486 - loss: 0.8296
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6485 - loss: 0.8298
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6484 - loss: 0.8301
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6483 - loss: 0.8303
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6482 - loss: 0.8305
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6481 - loss: 0.8307
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6480 - loss: 0.8309
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6479 - loss: 0.8311
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6478 - loss: 0.8313
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6478 - loss: 0.8314
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6477 - loss: 0.8316
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6476 - loss: 0.8317
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6475 - loss: 0.8319
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6475 - loss: 0.8320
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6474 - loss: 0.8321
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6474 - loss: 0.8322
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6473 - loss: 0.8323
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6473 - loss: 0.8324
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6472 - loss: 0.8324
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6472 - loss: 0.8325
Epoch 8: val_accuracy did not improve from 0.68957
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6471 - loss: 0.8326 - val_accuracy: 0.6637 - val_loss: 0.8172 - learning_rate: 0.0010
Epoch 9/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.7500 - loss: 0.7112
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7065 - loss: 0.7224
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6903 - loss: 0.7391
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6803 - loss: 0.7517
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6765 - loss: 0.7606
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6752 - loss: 0.7673
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6735 - loss: 0.7736
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6713 - loss: 0.7792
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6691 - loss: 0.7836
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6677 - loss: 0.7867
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6665 - loss: 0.7891
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6656 - loss: 0.7912
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6648 - loss: 0.7932
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6641 - loss: 0.7950
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6633 - loss: 0.7974
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6626 - loss: 0.7993
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6622 - loss: 0.8008
86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6621 - loss: 0.8017
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6620 - loss: 0.8026
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6619 - loss: 0.8039
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6618 - loss: 0.8050
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6616 - loss: 0.8062
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6614 - loss: 0.8073
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6611 - loss: 0.8083
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6608 - loss: 0.8095
126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6605 - loss: 0.8106
131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6602 - loss: 0.8117
136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6600 - loss: 0.8127
141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6598 - loss: 0.8137
146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6596 - loss: 0.8144
151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6594 - loss: 0.8152
156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6592 - loss: 0.8159
161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6591 - loss: 0.8165
166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6591 - loss: 0.8170
171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6590 - loss: 0.8176
176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6590 - loss: 0.8181
181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6589 - loss: 0.8187
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6588 - loss: 0.8191
191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6587 - loss: 0.8197
196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6586 - loss: 0.8201
201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6584 - loss: 0.8207
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6582 - loss: 0.8212
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6581 - loss: 0.8217
216/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6580 - loss: 0.8221
221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6579 - loss: 0.8225
226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6578 - loss: 0.8228
230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6577 - loss: 0.8231
235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6576 - loss: 0.8235
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6575 - loss: 0.8238
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6574 - loss: 0.8241
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6574 - loss: 0.8244
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6572 - loss: 0.8247
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6571 - loss: 0.8250
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6570 - loss: 0.8253
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6569 - loss: 0.8256
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6568 - loss: 0.8259
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6567 - loss: 0.8261
280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6566 - loss: 0.8264
284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6565 - loss: 0.8266
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6564 - loss: 0.8268
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6564 - loss: 0.8270
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6563 - loss: 0.8272
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6562 - loss: 0.8274
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6561 - loss: 0.8276
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6560 - loss: 0.8278
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6559 - loss: 0.8280
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6558 - loss: 0.8282
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6557 - loss: 0.8284
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6556 - loss: 0.8285
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6555 - loss: 0.8287
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6554 - loss: 0.8289
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6553 - loss: 0.8290
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6553 - loss: 0.8292
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6552 - loss: 0.8293
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6551 - loss: 0.8295
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6550 - loss: 0.8296
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6549 - loss: 0.8297
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6549 - loss: 0.8298
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6548 - loss: 0.8299
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6547 - loss: 0.8300
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6547 - loss: 0.8300
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6546 - loss: 0.8301
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6546 - loss: 0.8302
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6545 - loss: 0.8303
411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6544 - loss: 0.8304
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6544 - loss: 0.8304
421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6543 - loss: 0.8305
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6543 - loss: 0.8306
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6542 - loss: 0.8306
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6542 - loss: 0.8307
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6541 - loss: 0.8307
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6541 - loss: 0.8308
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6541 - loss: 0.8309
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6540 - loss: 0.8309
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6540 - loss: 0.8310
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6539 - loss: 0.8310
472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6539 - loss: 0.8311
Epoch 9: val_accuracy did not improve from 0.68957
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6539 - loss: 0.8311 - val_accuracy: 0.6323 - val_loss: 0.8551 - learning_rate: 0.0010
Epoch 10/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.6250 - loss: 1.0567
5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6631 - loss: 0.9727
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6865 - loss: 0.8890
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6972 - loss: 0.8538
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6997 - loss: 0.8367
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6991 - loss: 0.8268
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6977 - loss: 0.8203
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6964 - loss: 0.8152
39/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6949 - loss: 0.8121
44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6938 - loss: 0.8083
49/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6925 - loss: 0.8061
54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6911 - loss: 0.8042
59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6897 - loss: 0.8031
64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6885 - loss: 0.8020
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6876 - loss: 0.8008
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6868 - loss: 0.8007
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6859 - loss: 0.8011
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6849 - loss: 0.8020
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6838 - loss: 0.8029
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6829 - loss: 0.8036
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6819 - loss: 0.8042
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6809 - loss: 0.8050
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6801 - loss: 0.8057
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6794 - loss: 0.8062
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6788 - loss: 0.8067
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6783 - loss: 0.8071
129/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6778 - loss: 0.8076
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6772 - loss: 0.8082
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6766 - loss: 0.8088
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6760 - loss: 0.8093
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6755 - loss: 0.8098
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6750 - loss: 0.8103
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6746 - loss: 0.8107
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6742 - loss: 0.8110
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6739 - loss: 0.8114
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6735 - loss: 0.8117
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6732 - loss: 0.8119
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6728 - loss: 0.8122
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6725 - loss: 0.8124
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8127
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6718 - loss: 0.8129
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6715 - loss: 0.8131
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6712 - loss: 0.8133
214/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6709 - loss: 0.8135
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6706 - loss: 0.8137
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6703 - loss: 0.8139
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6700 - loss: 0.8141
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6698 - loss: 0.8143
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6695 - loss: 0.8144
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6693 - loss: 0.8145
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6692 - loss: 0.8146
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6690 - loss: 0.8146
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6689 - loss: 0.8147
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6687 - loss: 0.8148
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6686 - loss: 0.8148
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6684 - loss: 0.8149
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6683 - loss: 0.8150
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6682 - loss: 0.8150
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6681 - loss: 0.8151
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6679 - loss: 0.8152
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6678 - loss: 0.8152
300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6678 - loss: 0.8152
305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6677 - loss: 0.8153
310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6675 - loss: 0.8153
315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6674 - loss: 0.8154
320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6673 - loss: 0.8155
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6672 - loss: 0.8156
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6671 - loss: 0.8156
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6670 - loss: 0.8157
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6669 - loss: 0.8158
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6668 - loss: 0.8158
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.8159
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.8159
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.8160
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.8160
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6664 - loss: 0.8160
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6664 - loss: 0.8161
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6663 - loss: 0.8161
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6663 - loss: 0.8161
389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.8161
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.8161
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6661 - loss: 0.8160
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6661 - loss: 0.8160
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6661 - loss: 0.8160
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6660 - loss: 0.8160
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6660 - loss: 0.8160
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6659 - loss: 0.8160
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6658 - loss: 0.8160
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6658 - loss: 0.8160
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6657 - loss: 0.8160
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6657 - loss: 0.8160
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6656 - loss: 0.8160
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6656 - loss: 0.8160
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6655 - loss: 0.8160
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6654 - loss: 0.8161
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6654 - loss: 0.8161
Epoch 10: val_accuracy did not improve from 0.68957
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6653 - loss: 0.8162 - val_accuracy: 0.6337 - val_loss: 0.8556 - learning_rate: 0.0010
Epoch 11/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 106ms/step - accuracy: 0.6875 - loss: 0.7334
5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6606 - loss: 0.8048
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6520 - loss: 0.8091
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6517 - loss: 0.8120
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6522 - loss: 0.8124
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6539 - loss: 0.8131
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6545 - loss: 0.8135
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6539 - loss: 0.8165
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6530 - loss: 0.8197
45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6525 - loss: 0.8218
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6524 - loss: 0.8229
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6526 - loss: 0.8236
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6532 - loss: 0.8238
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6539 - loss: 0.8235
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6548 - loss: 0.8231
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6556 - loss: 0.8230
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6563 - loss: 0.8226
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6568 - loss: 0.8225
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6571 - loss: 0.8226
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6574 - loss: 0.8227
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6575 - loss: 0.8227
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6578 - loss: 0.8227
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6580 - loss: 0.8228
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6581 - loss: 0.8228
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6583 - loss: 0.8227
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6584 - loss: 0.8225
130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6586 - loss: 0.8223
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6588 - loss: 0.8220
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6590 - loss: 0.8217
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6592 - loss: 0.8215
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6593 - loss: 0.8213
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6595 - loss: 0.8211
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6596 - loss: 0.8209
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6597 - loss: 0.8206
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6597 - loss: 0.8205
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6596 - loss: 0.8204
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6595 - loss: 0.8205
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6594 - loss: 0.8206
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6593 - loss: 0.8206
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6592 - loss: 0.8206
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6592 - loss: 0.8206
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6591 - loss: 0.8205
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6591 - loss: 0.8205
214/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6590 - loss: 0.8204
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6590 - loss: 0.8203
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6589 - loss: 0.8202
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6589 - loss: 0.8202
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6589 - loss: 0.8201
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6588 - loss: 0.8200
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6588 - loss: 0.8199
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6587 - loss: 0.8199
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6586 - loss: 0.8199
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6586 - loss: 0.8199
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6585 - loss: 0.8199
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6584 - loss: 0.8199
274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6583 - loss: 0.8199
279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6582 - loss: 0.8198
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Epoch 11: val_accuracy improved from 0.68957 to 0.69781, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6580 - loss: 0.8165 - val_accuracy: 0.6978 - val_loss: 0.7402 - learning_rate: 0.0010
Epoch 12/45
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Epoch 12: val_accuracy improved from 0.69781 to 0.71569, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6732 - loss: 0.7880 - val_accuracy: 0.7157 - val_loss: 0.7013 - learning_rate: 0.0010
Epoch 13/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.6875 - loss: 0.7460
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6643 - loss: 0.8217
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6538 - loss: 0.8278
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6513 - loss: 0.8243
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6513 - loss: 0.8209
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6514 - loss: 0.8178
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226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6661 - loss: 0.7903
232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6662 - loss: 0.7901
237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6663 - loss: 0.7900
242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6664 - loss: 0.7898
247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6666 - loss: 0.7896
252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6667 - loss: 0.7894
257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6668 - loss: 0.7893
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277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6674 - loss: 0.7887
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6675 - loss: 0.7886
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292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6678 - loss: 0.7883
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302/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6681 - loss: 0.7880
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6682 - loss: 0.7879
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6683 - loss: 0.7877
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6684 - loss: 0.7876
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6686 - loss: 0.7875
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6687 - loss: 0.7874
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6688 - loss: 0.7872
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6689 - loss: 0.7871
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6690 - loss: 0.7870
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6691 - loss: 0.7869
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6693 - loss: 0.7867
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6694 - loss: 0.7866
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6695 - loss: 0.7865
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372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6697 - loss: 0.7863
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397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6701 - loss: 0.7860
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407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6702 - loss: 0.7859
412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6703 - loss: 0.7858
417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6703 - loss: 0.7858
422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6704 - loss: 0.7857
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6705 - loss: 0.7857
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6705 - loss: 0.7857
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6706 - loss: 0.7856
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6706 - loss: 0.7856
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6707 - loss: 0.7856
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6707 - loss: 0.7856
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6708 - loss: 0.7856
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6708 - loss: 0.7856
467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6709 - loss: 0.7856
Epoch 13: val_accuracy did not improve from 0.71569
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6709 - loss: 0.7856 - val_accuracy: 0.6835 - val_loss: 0.7741 - learning_rate: 0.0010
Epoch 14/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 106ms/step - accuracy: 0.7188 - loss: 0.9271
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6854 - loss: 0.8450
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6876 - loss: 0.8176
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6870 - loss: 0.8018
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6866 - loss: 0.7947
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6831 - loss: 0.7928
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6808 - loss: 0.7901
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6809 - loss: 0.7869
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6802 - loss: 0.7863
44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6795 - loss: 0.7862
49/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6786 - loss: 0.7857
53/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6781 - loss: 0.7856
58/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6777 - loss: 0.7862
62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6774 - loss: 0.7868
67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6766 - loss: 0.7879
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6760 - loss: 0.7886
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6754 - loss: 0.7894
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6749 - loss: 0.7900
86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6747 - loss: 0.7904
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6747 - loss: 0.7905
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6746 - loss: 0.7906
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6744 - loss: 0.7908
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6743 - loss: 0.7909
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6742 - loss: 0.7909
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6742 - loss: 0.7909
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6743 - loss: 0.7908
126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6744 - loss: 0.7907
131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6744 - loss: 0.7906
136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6746 - loss: 0.7904
141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6747 - loss: 0.7900
146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6749 - loss: 0.7896
151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6750 - loss: 0.7893
156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6751 - loss: 0.7890
161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6751 - loss: 0.7889
166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6751 - loss: 0.7888
171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6751 - loss: 0.7887
176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6751 - loss: 0.7886
181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6750 - loss: 0.7885
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6750 - loss: 0.7885
191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6749 - loss: 0.7884
196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6749 - loss: 0.7884
201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6748 - loss: 0.7884
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6748 - loss: 0.7883
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6748 - loss: 0.7883
216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6748 - loss: 0.7882
220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6748 - loss: 0.7881
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6748 - loss: 0.7880
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6748 - loss: 0.7879
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6748 - loss: 0.7878
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6748 - loss: 0.7877
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6748 - loss: 0.7877
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6747 - loss: 0.7877
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6747 - loss: 0.7877
257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6746 - loss: 0.7878
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6746 - loss: 0.7878
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6745 - loss: 0.7878
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6745 - loss: 0.7879
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6745 - loss: 0.7879
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6745 - loss: 0.7879
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6744 - loss: 0.7880
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6744 - loss: 0.7881
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6743 - loss: 0.7882
302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6743 - loss: 0.7883
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6742 - loss: 0.7884
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6742 - loss: 0.7885
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6742 - loss: 0.7885
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6741 - loss: 0.7886
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6741 - loss: 0.7886
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6741 - loss: 0.7886
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6741 - loss: 0.7887
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6741 - loss: 0.7887
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7887
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7887
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7887
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7886
411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7886
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7885
421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7885
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7885
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7884
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7884
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7884
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7883
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7883
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7883
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7883
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7883
472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7883
Epoch 14: val_accuracy did not improve from 0.71569
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6740 - loss: 0.7883 - val_accuracy: 0.6618 - val_loss: 0.8420 - learning_rate: 0.0010
Epoch 15/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 56s 120ms/step - accuracy: 0.5625 - loss: 0.8298
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6201 - loss: 0.8163
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6340 - loss: 0.8172
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6443 - loss: 0.8162
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6511 - loss: 0.8115
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6546 - loss: 0.8095
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6556 - loss: 0.8088
34/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6558 - loss: 0.8077
38/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6566 - loss: 0.8054
42/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6573 - loss: 0.8032
47/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6576 - loss: 0.8008
52/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6577 - loss: 0.7987
57/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6576 - loss: 0.7968
62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6576 - loss: 0.7953
67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6575 - loss: 0.7945
72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6575 - loss: 0.7937
78/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6576 - loss: 0.7930
82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6577 - loss: 0.7926
87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6577 - loss: 0.7924
92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6578 - loss: 0.7923
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6580 - loss: 0.7923
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6583 - loss: 0.7920
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6586 - loss: 0.7916
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6590 - loss: 0.7912
113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6593 - loss: 0.7909
117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6596 - loss: 0.7907
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6598 - loss: 0.7905
126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6601 - loss: 0.7903
131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6605 - loss: 0.7899
136/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6610 - loss: 0.7894
141/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6614 - loss: 0.7890
146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6617 - loss: 0.7886
151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6619 - loss: 0.7883
156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6622 - loss: 0.7881
161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6624 - loss: 0.7879
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6626 - loss: 0.7877
170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6629 - loss: 0.7874
175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6631 - loss: 0.7871
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6634 - loss: 0.7868
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6637 - loss: 0.7865
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6640 - loss: 0.7861
195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6642 - loss: 0.7858
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6644 - loss: 0.7855
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6646 - loss: 0.7853
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6648 - loss: 0.7850
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6651 - loss: 0.7847
218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6653 - loss: 0.7844
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6655 - loss: 0.7842
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6657 - loss: 0.7839
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6660 - loss: 0.7836
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7834
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6664 - loss: 0.7832
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6666 - loss: 0.7830
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6668 - loss: 0.7827
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6670 - loss: 0.7826
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6672 - loss: 0.7825
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6673 - loss: 0.7824
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6675 - loss: 0.7823
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6677 - loss: 0.7822
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6678 - loss: 0.7821
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6680 - loss: 0.7819
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6682 - loss: 0.7818
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6683 - loss: 0.7817
303/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6685 - loss: 0.7816
308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6686 - loss: 0.7815
313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6688 - loss: 0.7813
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6689 - loss: 0.7812
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6691 - loss: 0.7811
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6692 - loss: 0.7810
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6693 - loss: 0.7809
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6694 - loss: 0.7809
343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6695 - loss: 0.7808
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6696 - loss: 0.7808
353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6697 - loss: 0.7808
358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6697 - loss: 0.7807
363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6698 - loss: 0.7807
368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6698 - loss: 0.7807
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6699 - loss: 0.7807
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6700 - loss: 0.7806
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6700 - loss: 0.7806
388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6701 - loss: 0.7806
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6701 - loss: 0.7806
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6701 - loss: 0.7805
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6702 - loss: 0.7805
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6702 - loss: 0.7805
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6703 - loss: 0.7805
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6703 - loss: 0.7804
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6704 - loss: 0.7804
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6704 - loss: 0.7804
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6705 - loss: 0.7804
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6705 - loss: 0.7804
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6706 - loss: 0.7803
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6706 - loss: 0.7803
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6707 - loss: 0.7803
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6707 - loss: 0.7803
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6708 - loss: 0.7803
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6709 - loss: 0.7803
Epoch 15: val_accuracy did not improve from 0.71569
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6709 - loss: 0.7802 - val_accuracy: 0.7113 - val_loss: 0.7071 - learning_rate: 0.0010
Epoch 16/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 106ms/step - accuracy: 0.7188 - loss: 0.8237
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7056 - loss: 0.7728
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6908 - loss: 0.7747
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6835 - loss: 0.7797
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6803 - loss: 0.7816
24/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6780 - loss: 0.7809
29/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6750 - loss: 0.7806
34/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6729 - loss: 0.7806
39/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6718 - loss: 0.7805
44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6719 - loss: 0.7793
49/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6724 - loss: 0.7782
54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6730 - loss: 0.7774
59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6737 - loss: 0.7767
64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6740 - loss: 0.7766
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6744 - loss: 0.7764
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6747 - loss: 0.7763
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6750 - loss: 0.7762
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6754 - loss: 0.7760
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6757 - loss: 0.7759
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6760 - loss: 0.7756
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6764 - loss: 0.7752
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6766 - loss: 0.7747
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6767 - loss: 0.7745
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6768 - loss: 0.7743
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6769 - loss: 0.7742
123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6769 - loss: 0.7742
128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6769 - loss: 0.7744
133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6769 - loss: 0.7747
138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6768 - loss: 0.7749
143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6768 - loss: 0.7750
148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6767 - loss: 0.7752
153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6767 - loss: 0.7753
158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6766 - loss: 0.7754
163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6766 - loss: 0.7755
168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6766 - loss: 0.7755
173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6765 - loss: 0.7756
178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6765 - loss: 0.7756
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6765 - loss: 0.7756
188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6766 - loss: 0.7756
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6766 - loss: 0.7756
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6766 - loss: 0.7756
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6767 - loss: 0.7756
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6767 - loss: 0.7756
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6767 - loss: 0.7757
218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6768 - loss: 0.7757
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6769 - loss: 0.7757
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6769 - loss: 0.7756
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6770 - loss: 0.7756
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6771 - loss: 0.7755
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6772 - loss: 0.7755
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6772 - loss: 0.7755
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6773 - loss: 0.7756
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6773 - loss: 0.7756
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6773 - loss: 0.7756
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6774 - loss: 0.7756
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6774 - loss: 0.7756
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7756
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7756
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7755
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6776 - loss: 0.7755
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6777 - loss: 0.7755
303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6777 - loss: 0.7754
308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6778 - loss: 0.7754
313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6778 - loss: 0.7753
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6779 - loss: 0.7752
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6779 - loss: 0.7752
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6780 - loss: 0.7751
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6781 - loss: 0.7751
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6781 - loss: 0.7750
343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6782 - loss: 0.7749
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6782 - loss: 0.7749
353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6783 - loss: 0.7748
358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6783 - loss: 0.7748
363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6784 - loss: 0.7747
368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6784 - loss: 0.7747
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6784 - loss: 0.7747
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6785 - loss: 0.7746
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6785 - loss: 0.7746
388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7746
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7746
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7746
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7746
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6786 - loss: 0.7746
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6786 - loss: 0.7747
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6786 - loss: 0.7747
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6786 - loss: 0.7747
Epoch 16: val_accuracy did not improve from 0.71569
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6786 - loss: 0.7747 - val_accuracy: 0.6803 - val_loss: 0.7580 - learning_rate: 0.0010
Epoch 17/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 102ms/step - accuracy: 0.6875 - loss: 0.9330
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7222 - loss: 0.7754
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7059 - loss: 0.7731
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6981 - loss: 0.7764
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6935 - loss: 0.7768
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6907 - loss: 0.7767
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6884 - loss: 0.7774
34/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6863 - loss: 0.7788
39/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6841 - loss: 0.7794
42/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6830 - loss: 0.7798
47/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6817 - loss: 0.7800
52/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6806 - loss: 0.7802
57/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6795 - loss: 0.7805
62/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6788 - loss: 0.7801
67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6786 - loss: 0.7793
72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6787 - loss: 0.7785
77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6786 - loss: 0.7781
82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6785 - loss: 0.7777
87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6784 - loss: 0.7775
92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6785 - loss: 0.7772
97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6786 - loss: 0.7770
102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6787 - loss: 0.7768
107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6787 - loss: 0.7767
112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6787 - loss: 0.7765
117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6787 - loss: 0.7762
122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6788 - loss: 0.7759
126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6788 - loss: 0.7758
130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6789 - loss: 0.7755
135/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6789 - loss: 0.7753
140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6789 - loss: 0.7752
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6788 - loss: 0.7752
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6787 - loss: 0.7752
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6787 - loss: 0.7751
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6787 - loss: 0.7750
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6787 - loss: 0.7749
170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6786 - loss: 0.7749
175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6786 - loss: 0.7747
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6786 - loss: 0.7745
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6786 - loss: 0.7744
191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6786 - loss: 0.7742
196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6785 - loss: 0.7741
201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6785 - loss: 0.7740
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6784 - loss: 0.7740
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6783 - loss: 0.7740
216/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6782 - loss: 0.7740
221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6781 - loss: 0.7741
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6780 - loss: 0.7741
230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6779 - loss: 0.7741
235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6778 - loss: 0.7741
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6777 - loss: 0.7740
245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6777 - loss: 0.7740
250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6776 - loss: 0.7740
255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6776 - loss: 0.7739
260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6776 - loss: 0.7739
265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6776 - loss: 0.7738
270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7737
275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7737
280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7736
285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7735
290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7734
294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6776 - loss: 0.7734
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6776 - loss: 0.7733
303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6776 - loss: 0.7732
308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6777 - loss: 0.7731
313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6777 - loss: 0.7730
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6777 - loss: 0.7729
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6778 - loss: 0.7728
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6778 - loss: 0.7727
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6778 - loss: 0.7726
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6779 - loss: 0.7725
343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6779 - loss: 0.7724
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6779 - loss: 0.7723
353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6779 - loss: 0.7722
358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6780 - loss: 0.7721
363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6780 - loss: 0.7720
368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6781 - loss: 0.7718
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6781 - loss: 0.7718
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6781 - loss: 0.7717
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6782 - loss: 0.7716
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6782 - loss: 0.7715
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6782 - loss: 0.7714
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6782 - loss: 0.7714
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6783 - loss: 0.7713
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6783 - loss: 0.7712
410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6783 - loss: 0.7711
415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6784 - loss: 0.7710
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Epoch 17: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.
Epoch 17: val_accuracy did not improve from 0.71569
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6785 - loss: 0.7704 - val_accuracy: 0.6797 - val_loss: 0.7835 - learning_rate: 0.0010
Epoch 18/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 104ms/step - accuracy: 0.6562 - loss: 0.8476
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7261 - loss: 0.7122
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7198 - loss: 0.7154
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7125 - loss: 0.7286
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101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7018 - loss: 0.7441
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226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6999 - loss: 0.7387
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6999 - loss: 0.7386
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6998 - loss: 0.7385
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6997 - loss: 0.7383
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6997 - loss: 0.7382
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315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6996 - loss: 0.7356
320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6997 - loss: 0.7354
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6997 - loss: 0.7352
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6997 - loss: 0.7350
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6997 - loss: 0.7348
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6997 - loss: 0.7347
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6998 - loss: 0.7345
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6998 - loss: 0.7343
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6998 - loss: 0.7341
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363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6998 - loss: 0.7339
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446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7001 - loss: 0.7313
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7002 - loss: 0.7311
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7002 - loss: 0.7310
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7002 - loss: 0.7309
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7002 - loss: 0.7308
471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7002 - loss: 0.7307
Epoch 18: val_accuracy improved from 0.71569 to 0.73418, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7002 - loss: 0.7306 - val_accuracy: 0.7342 - val_loss: 0.6616 - learning_rate: 2.0000e-04
Epoch 19/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 107ms/step - accuracy: 0.7500 - loss: 0.6538
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6948 - loss: 0.7046
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6834 - loss: 0.7137
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6885 - loss: 0.7018
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6907 - loss: 0.6961
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6928 - loss: 0.6946
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35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6988 - loss: 0.6906
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44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7023 - loss: 0.6885
49/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7044 - loss: 0.6870
54/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7059 - loss: 0.6863
59/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7069 - loss: 0.6859
64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7077 - loss: 0.6857
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7087 - loss: 0.6853
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7096 - loss: 0.6846
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7103 - loss: 0.6846
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7107 - loss: 0.6851
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7111 - loss: 0.6854
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7113 - loss: 0.6856
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7115 - loss: 0.6861
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7116 - loss: 0.6866
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7116 - loss: 0.6872
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7116 - loss: 0.6878
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7116 - loss: 0.6884
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7117 - loss: 0.6889
127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7118 - loss: 0.6892
130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7119 - loss: 0.6894
133/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7120 - loss: 0.6897
138/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7121 - loss: 0.6902
143/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7122 - loss: 0.6905
148/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7123 - loss: 0.6907
152/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7125 - loss: 0.6908
157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7126 - loss: 0.6910
162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7127 - loss: 0.6911
167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7128 - loss: 0.6913
171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7128 - loss: 0.6914
175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7129 - loss: 0.6915
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.7130 - loss: 0.6916
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.7131 - loss: 0.6917
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7131 - loss: 0.6918
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7132 - loss: 0.6919
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7132 - loss: 0.6920
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7132 - loss: 0.6922
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7132 - loss: 0.6923
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7132 - loss: 0.6925
218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7131 - loss: 0.6928
222/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7131 - loss: 0.6930
227/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7131 - loss: 0.6932
231/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7130 - loss: 0.6934
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6936
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6937
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6938
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6939
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6941
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6942
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6943
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6945
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7129 - loss: 0.6946
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7129 - loss: 0.6948
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7129 - loss: 0.6949
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7128 - loss: 0.6951
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7128 - loss: 0.6953
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7127 - loss: 0.6955
303/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7126 - loss: 0.6957
308/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7126 - loss: 0.6959
313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7125 - loss: 0.6960
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7124 - loss: 0.6962
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7123 - loss: 0.6964
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7123 - loss: 0.6966
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7122 - loss: 0.6967
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7122 - loss: 0.6969
343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7121 - loss: 0.6970
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7121 - loss: 0.6971
353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7120 - loss: 0.6972
358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7120 - loss: 0.6974
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7120 - loss: 0.6975
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7119 - loss: 0.6975
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7119 - loss: 0.6976
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7119 - loss: 0.6977
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7118 - loss: 0.6978
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7118 - loss: 0.6979
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7118 - loss: 0.6980
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7118 - loss: 0.6980
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7118 - loss: 0.6981
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7117 - loss: 0.6982
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7117 - loss: 0.6982
415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7117 - loss: 0.6983
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7117 - loss: 0.6984
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7117 - loss: 0.6985
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7116 - loss: 0.6985
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7116 - loss: 0.6986
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7116 - loss: 0.6987
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7115 - loss: 0.6987
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7115 - loss: 0.6988
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7115 - loss: 0.6989
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7114 - loss: 0.6989
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7114 - loss: 0.6990
471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7114 - loss: 0.6992
Epoch 19: val_accuracy did not improve from 0.73418
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7113 - loss: 0.6992 - val_accuracy: 0.7265 - val_loss: 0.6837 - learning_rate: 2.0000e-04
Epoch 20/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 53s 114ms/step - accuracy: 0.6562 - loss: 0.7046
5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6496 - loss: 0.7652
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6531 - loss: 0.7556
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6555 - loss: 0.7588
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6587 - loss: 0.7646
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6625 - loss: 0.7655
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6676 - loss: 0.7607
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6711 - loss: 0.7565
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6739 - loss: 0.7525
45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6759 - loss: 0.7497
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6775 - loss: 0.7474
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6789 - loss: 0.7449
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6801 - loss: 0.7424
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6812 - loss: 0.7402
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6822 - loss: 0.7382
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6830 - loss: 0.7370
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6835 - loss: 0.7362
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6840 - loss: 0.7355
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6845 - loss: 0.7348
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6849 - loss: 0.7342
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6855 - loss: 0.7336
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6861 - loss: 0.7329
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6865 - loss: 0.7324
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6870 - loss: 0.7319
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6875 - loss: 0.7314
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6880 - loss: 0.7310
129/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6885 - loss: 0.7306
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6890 - loss: 0.7303
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6894 - loss: 0.7300
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6897 - loss: 0.7298
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6901 - loss: 0.7295
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6906 - loss: 0.7289
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6909 - loss: 0.7285
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6913 - loss: 0.7280
170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6917 - loss: 0.7275
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Epoch 20: val_accuracy improved from 0.73418 to 0.74020, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7039 - loss: 0.7124 - val_accuracy: 0.7402 - val_loss: 0.6452 - learning_rate: 2.0000e-04
Epoch 21/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:02 131ms/step - accuracy: 0.6875 - loss: 0.6260
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283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6951
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6953
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6954
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6956
302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6956
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.6958
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.6959
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.6960
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.6962
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.6963
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.6964
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7114 - loss: 0.6965
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7114 - loss: 0.6966
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7114 - loss: 0.6967
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7114 - loss: 0.6967
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7114 - loss: 0.6968
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7113 - loss: 0.6969
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7113 - loss: 0.6970
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7113 - loss: 0.6971
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7112 - loss: 0.6972
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7112 - loss: 0.6973
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7112 - loss: 0.6973
390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7111 - loss: 0.6974
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7111 - loss: 0.6975
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7111 - loss: 0.6976
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7111 - loss: 0.6977
410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7110 - loss: 0.6978
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7110 - loss: 0.6978
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7110 - loss: 0.6979
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7110 - loss: 0.6980
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7110 - loss: 0.6980
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6981
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6981
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6982
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6982
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6983
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6983
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6984
467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6984
Epoch 21: val_accuracy did not improve from 0.74020
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7109 - loss: 0.6985 - val_accuracy: 0.7346 - val_loss: 0.6472 - learning_rate: 2.0000e-04
Epoch 22/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 54s 116ms/step - accuracy: 0.7500 - loss: 0.6675
4/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.6849 - loss: 0.6972
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 17ms/step - accuracy: 0.6790 - loss: 0.7014
12/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6842 - loss: 0.7015
17/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.6869 - loss: 0.7056
22/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6879 - loss: 0.7112
27/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6892 - loss: 0.7147
32/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6911 - loss: 0.7155
37/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6922 - loss: 0.7168
42/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6930 - loss: 0.7172
47/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6937 - loss: 0.7174
52/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6945 - loss: 0.7174
57/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6952 - loss: 0.7171
62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6960 - loss: 0.7167
67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6968 - loss: 0.7161
72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6973 - loss: 0.7158
77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6979 - loss: 0.7154
82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6985 - loss: 0.7146
87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6994 - loss: 0.7135
92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7002 - loss: 0.7124
97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7010 - loss: 0.7114
103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7018 - loss: 0.7104
108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7025 - loss: 0.7094
113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7032 - loss: 0.7086
118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7038 - loss: 0.7078
123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7045 - loss: 0.7070
128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7050 - loss: 0.7064
133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7056 - loss: 0.7059
138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7062 - loss: 0.7053
143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7068 - loss: 0.7047
148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7074 - loss: 0.7041
153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7080 - loss: 0.7034
158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7085 - loss: 0.7028
163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7090 - loss: 0.7023
168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7095 - loss: 0.7018
173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7099 - loss: 0.7014
178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7103 - loss: 0.7009
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7107 - loss: 0.7005
188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7110 - loss: 0.7001
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7113 - loss: 0.6998
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7115 - loss: 0.6996
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6994
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7119 - loss: 0.6992
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7121 - loss: 0.6990
218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7123 - loss: 0.6988
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7125 - loss: 0.6987
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7127 - loss: 0.6985
232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7128 - loss: 0.6983
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6982
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7131 - loss: 0.6981
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7133 - loss: 0.6979
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7134 - loss: 0.6978
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7136 - loss: 0.6976
261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7138 - loss: 0.6975
266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7139 - loss: 0.6973
271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7141 - loss: 0.6972
275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7141 - loss: 0.6971
280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7143 - loss: 0.6970
284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7143 - loss: 0.6969
289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7144 - loss: 0.6968
294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7145 - loss: 0.6968
299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7146 - loss: 0.6967
304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7147 - loss: 0.6967
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7147 - loss: 0.6966
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7148 - loss: 0.6966
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7149 - loss: 0.6965
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7149 - loss: 0.6965
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7150 - loss: 0.6964
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7151 - loss: 0.6964
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7151 - loss: 0.6964
343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7152 - loss: 0.6964
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7152 - loss: 0.6964
353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7153 - loss: 0.6964
358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7153 - loss: 0.6964
363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7153 - loss: 0.6964
367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7154 - loss: 0.6964
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7154 - loss: 0.6964
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7154 - loss: 0.6964
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7154 - loss: 0.6964
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7155 - loss: 0.6964
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7155 - loss: 0.6964
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7155 - loss: 0.6963
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7155 - loss: 0.6963
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7156 - loss: 0.6963
411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7156 - loss: 0.6963
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459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7158 - loss: 0.6963
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Epoch 22: val_accuracy did not improve from 0.74020
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7158 - loss: 0.6962 - val_accuracy: 0.7388 - val_loss: 0.6436 - learning_rate: 2.0000e-04
Epoch 23/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 104ms/step - accuracy: 0.7812 - loss: 0.6125
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7504 - loss: 0.6771
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7368 - loss: 0.6731
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7335 - loss: 0.6652
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7309 - loss: 0.6642
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7280 - loss: 0.6672
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7263 - loss: 0.6693
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7248 - loss: 0.6715
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71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7210 - loss: 0.6789
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81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7204 - loss: 0.6804
86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7201 - loss: 0.6811
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7199 - loss: 0.6817
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7197 - loss: 0.6821
98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7196 - loss: 0.6825
103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7194 - loss: 0.6830
108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7194 - loss: 0.6831
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133/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7196 - loss: 0.6839
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141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7197 - loss: 0.6841
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151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7198 - loss: 0.6846
156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7198 - loss: 0.6849
161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7197 - loss: 0.6852
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210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7191 - loss: 0.6877
215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7190 - loss: 0.6879
220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7190 - loss: 0.6882
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7189 - loss: 0.6883
230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7188 - loss: 0.6885
235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7187 - loss: 0.6886
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7187 - loss: 0.6887
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7186 - loss: 0.6889
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7186 - loss: 0.6890
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276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7184 - loss: 0.6894
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291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7184 - loss: 0.6896
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7184 - loss: 0.6897
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7183 - loss: 0.6897
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7183 - loss: 0.6898
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7182 - loss: 0.6899
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7182 - loss: 0.6900
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7181 - loss: 0.6901
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7180 - loss: 0.6902
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7180 - loss: 0.6902
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7179 - loss: 0.6903
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7178 - loss: 0.6904
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7178 - loss: 0.6905
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7177 - loss: 0.6906
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7176 - loss: 0.6907
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7175 - loss: 0.6908
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7174 - loss: 0.6909
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7173 - loss: 0.6910
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7172 - loss: 0.6911
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7171 - loss: 0.6912
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7170 - loss: 0.6913
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7169 - loss: 0.6914
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7168 - loss: 0.6915
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7168 - loss: 0.6916
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7167 - loss: 0.6917
411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7167 - loss: 0.6917
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7166 - loss: 0.6918
421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7165 - loss: 0.6919
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7165 - loss: 0.6920
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7164 - loss: 0.6921
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7164 - loss: 0.6922
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7163 - loss: 0.6923
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7162 - loss: 0.6924
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7162 - loss: 0.6924
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7161 - loss: 0.6925
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7161 - loss: 0.6926
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7160 - loss: 0.6927
Epoch 23: val_accuracy improved from 0.74020 to 0.74141, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7159 - loss: 0.6928 - val_accuracy: 0.7414 - val_loss: 0.6339 - learning_rate: 2.0000e-04
Epoch 24/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 106ms/step - accuracy: 0.7188 - loss: 0.6393
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7502 - loss: 0.6051
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7353 - loss: 0.6301
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7266 - loss: 0.6461
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7255 - loss: 0.6513
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7264 - loss: 0.6531
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7272 - loss: 0.6552
34/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7273 - loss: 0.6583
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44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7283 - loss: 0.6642
49/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7283 - loss: 0.6662
54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7283 - loss: 0.6673
59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7284 - loss: 0.6679
64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7287 - loss: 0.6681
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7290 - loss: 0.6679
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7292 - loss: 0.6680
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7290 - loss: 0.6684
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7287 - loss: 0.6690
88/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7286 - loss: 0.6694
93/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7286 - loss: 0.6696
98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7287 - loss: 0.6697
103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7286 - loss: 0.6697
108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7286 - loss: 0.6697
113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7285 - loss: 0.6698
118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7283 - loss: 0.6699
122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7282 - loss: 0.6701
127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7280 - loss: 0.6705
132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7278 - loss: 0.6708
137/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7276 - loss: 0.6711
142/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7275 - loss: 0.6714
147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6716
152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7273 - loss: 0.6718
157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7273 - loss: 0.6719
162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7273 - loss: 0.6720
167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7272 - loss: 0.6720
172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7272 - loss: 0.6721
176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7272 - loss: 0.6721
181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7271 - loss: 0.6722
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7271 - loss: 0.6722
191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7271 - loss: 0.6723
196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7270 - loss: 0.6724
201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7269 - loss: 0.6726
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7268 - loss: 0.6728
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7267 - loss: 0.6729
216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7266 - loss: 0.6731
221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7265 - loss: 0.6733
226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7264 - loss: 0.6734
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7263 - loss: 0.6735
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7262 - loss: 0.6736
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7262 - loss: 0.6737
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7261 - loss: 0.6738
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7260 - loss: 0.6739
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7259 - loss: 0.6740
261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7258 - loss: 0.6741
266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7257 - loss: 0.6743
271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7256 - loss: 0.6745
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7255 - loss: 0.6746
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7253 - loss: 0.6748
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7252 - loss: 0.6750
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7251 - loss: 0.6752
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7250 - loss: 0.6753
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7248 - loss: 0.6755
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7247 - loss: 0.6756
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7246 - loss: 0.6758
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7245 - loss: 0.6759
320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7245 - loss: 0.6760
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7243 - loss: 0.6762
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7242 - loss: 0.6764
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7242 - loss: 0.6765
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7241 - loss: 0.6766
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7240 - loss: 0.6768
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7239 - loss: 0.6770
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7238 - loss: 0.6771
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7237 - loss: 0.6773
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6775
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6776
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7234 - loss: 0.6778
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7234 - loss: 0.6779
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7233 - loss: 0.6780
389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7232 - loss: 0.6782
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7231 - loss: 0.6783
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7231 - loss: 0.6785
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7230 - loss: 0.6786
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7229 - loss: 0.6788
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7228 - loss: 0.6789
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7228 - loss: 0.6791
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7227 - loss: 0.6792
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7226 - loss: 0.6794
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7225 - loss: 0.6795
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7225 - loss: 0.6797
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7224 - loss: 0.6798
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7224 - loss: 0.6799
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7223 - loss: 0.6801
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7222 - loss: 0.6802
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7222 - loss: 0.6803
471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7221 - loss: 0.6805
Epoch 24: val_accuracy did not improve from 0.74141
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7221 - loss: 0.6805 - val_accuracy: 0.7376 - val_loss: 0.6382 - learning_rate: 2.0000e-04
Epoch 25/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.6562 - loss: 0.8090
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6897 - loss: 0.7138
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6961 - loss: 0.6986
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6980 - loss: 0.6967
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6988 - loss: 0.7005
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6982 - loss: 0.7034
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6993 - loss: 0.7033
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7003 - loss: 0.7031
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7005 - loss: 0.7039
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7005 - loss: 0.7047
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7007 - loss: 0.7047
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7010 - loss: 0.7039
59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7013 - loss: 0.7031
64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7019 - loss: 0.7023
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7025 - loss: 0.7013
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7031 - loss: 0.7004
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7037 - loss: 0.6995
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7044 - loss: 0.6984
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7051 - loss: 0.6973
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7057 - loss: 0.6965
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7062 - loss: 0.6959
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7067 - loss: 0.6952
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7071 - loss: 0.6946
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7077 - loss: 0.6940
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7082 - loss: 0.6934
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7085 - loss: 0.6930
129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7089 - loss: 0.6927
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7092 - loss: 0.6925
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7095 - loss: 0.6922
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7097 - loss: 0.6920
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7100 - loss: 0.6919
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7102 - loss: 0.6917
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7105 - loss: 0.6915
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7107 - loss: 0.6913
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7110 - loss: 0.6912
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7112 - loss: 0.6910
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7114 - loss: 0.6908
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7115 - loss: 0.6908
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7116 - loss: 0.6908
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7116 - loss: 0.6908
195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6908
200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6909
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6910
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6911
214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7118 - loss: 0.6911
220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7118 - loss: 0.6911
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7118 - loss: 0.6912
230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7119 - loss: 0.6913
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7119 - loss: 0.6913
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7120 - loss: 0.6914
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7120 - loss: 0.6913
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7121 - loss: 0.6913
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7122 - loss: 0.6913
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7123 - loss: 0.6913
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7123 - loss: 0.6913
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7124 - loss: 0.6913
274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7125 - loss: 0.6913
279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7126 - loss: 0.6913
284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7127 - loss: 0.6912
289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7128 - loss: 0.6912
294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7129 - loss: 0.6911
299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6911
304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7130 - loss: 0.6910
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7131 - loss: 0.6910
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7132 - loss: 0.6909
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7133 - loss: 0.6909
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7133 - loss: 0.6909
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7134 - loss: 0.6908
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7135 - loss: 0.6908
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7136 - loss: 0.6907
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7136 - loss: 0.6906
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7137 - loss: 0.6905
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7138 - loss: 0.6905
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7139 - loss: 0.6904
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7139 - loss: 0.6903
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7140 - loss: 0.6902
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7141 - loss: 0.6901
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7141 - loss: 0.6900
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7142 - loss: 0.6899
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7142 - loss: 0.6898
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7143 - loss: 0.6898
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7143 - loss: 0.6898
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7144 - loss: 0.6898
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7145 - loss: 0.6897
411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7145 - loss: 0.6897
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7146 - loss: 0.6897
421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7146 - loss: 0.6897
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7146 - loss: 0.6897
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7147 - loss: 0.6897
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7147 - loss: 0.6897
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7147 - loss: 0.6898
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7148 - loss: 0.6898
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7148 - loss: 0.6898
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7148 - loss: 0.6898
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7148 - loss: 0.6898
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7148 - loss: 0.6899
472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7149 - loss: 0.6899
Epoch 25: val_accuracy did not improve from 0.74141
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7149 - loss: 0.6899 - val_accuracy: 0.7376 - val_loss: 0.6535 - learning_rate: 2.0000e-04
Epoch 26/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 106ms/step - accuracy: 0.7500 - loss: 0.6739
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7461 - loss: 0.6826
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7484 - loss: 0.6843
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7427 - loss: 0.6857
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7388 - loss: 0.6892
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7360 - loss: 0.6903
31/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7351 - loss: 0.6894
36/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7338 - loss: 0.6892
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7327 - loss: 0.6883
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7318 - loss: 0.6875
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7313 - loss: 0.6865
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7309 - loss: 0.6855
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7303 - loss: 0.6848
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7296 - loss: 0.6845
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7291 - loss: 0.6843
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7289 - loss: 0.6839
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7286 - loss: 0.6838
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7284 - loss: 0.6835
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7282 - loss: 0.6830
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7282 - loss: 0.6825
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7281 - loss: 0.6822
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7280 - loss: 0.6819
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7278 - loss: 0.6817
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7277 - loss: 0.6815
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7276 - loss: 0.6814
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7275 - loss: 0.6813
130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7273 - loss: 0.6812
135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7272 - loss: 0.6811
140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7271 - loss: 0.6811
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7269 - loss: 0.6811
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7267 - loss: 0.6812
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7265 - loss: 0.6814
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7262 - loss: 0.6816
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7260 - loss: 0.6819
170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7257 - loss: 0.6822
175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7255 - loss: 0.6825
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7254 - loss: 0.6828
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7252 - loss: 0.6831
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7250 - loss: 0.6833
195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7249 - loss: 0.6835
200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7248 - loss: 0.6837
205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7247 - loss: 0.6838
210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7245 - loss: 0.6839
215/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7244 - loss: 0.6841
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7243 - loss: 0.6841
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7242 - loss: 0.6842
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7241 - loss: 0.6842
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7241 - loss: 0.6842
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7240 - loss: 0.6843
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7239 - loss: 0.6843
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7239 - loss: 0.6844
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7238 - loss: 0.6844
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7238 - loss: 0.6845
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7237 - loss: 0.6845
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7237 - loss: 0.6845
274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7236 - loss: 0.6846
279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7236 - loss: 0.6846
284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7236 - loss: 0.6846
289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7236 - loss: 0.6846
294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7235 - loss: 0.6846
299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7235 - loss: 0.6846
304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6846
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7234 - loss: 0.6846
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Epoch 26: val_accuracy improved from 0.74141 to 0.74844, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7212 - loss: 0.6871 - val_accuracy: 0.7484 - val_loss: 0.6241 - learning_rate: 2.0000e-04
Epoch 27/45
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432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7139 - loss: 0.6945
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7139 - loss: 0.6944
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7140 - loss: 0.6944
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7140 - loss: 0.6943
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7141 - loss: 0.6942
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7141 - loss: 0.6942
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7141 - loss: 0.6941
467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7142 - loss: 0.6940
Epoch 27: val_accuracy did not improve from 0.74844
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7143 - loss: 0.6939 - val_accuracy: 0.7450 - val_loss: 0.6338 - learning_rate: 2.0000e-04
Epoch 28/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.5938 - loss: 0.7196
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6997 - loss: 0.6371
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7130 - loss: 0.6291
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7204 - loss: 0.6282
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7226 - loss: 0.6314
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7250 - loss: 0.6342
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7261 - loss: 0.6394
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7266 - loss: 0.6439
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7274 - loss: 0.6461
45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7281 - loss: 0.6477
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7282 - loss: 0.6500
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7286 - loss: 0.6516
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7291 - loss: 0.6527
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7294 - loss: 0.6538
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7295 - loss: 0.6548
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7293 - loss: 0.6560
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7291 - loss: 0.6572
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7288 - loss: 0.6584
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7286 - loss: 0.6595
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7284 - loss: 0.6606
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7283 - loss: 0.6618
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7282 - loss: 0.6629
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7281 - loss: 0.6637
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7280 - loss: 0.6645
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7280 - loss: 0.6650
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7280 - loss: 0.6654
130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7279 - loss: 0.6657
135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7279 - loss: 0.6661
140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7278 - loss: 0.6666
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7277 - loss: 0.6670
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7277 - loss: 0.6673
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7276 - loss: 0.6676
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7276 - loss: 0.6678
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7275 - loss: 0.6682
170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7274 - loss: 0.6686
175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7273 - loss: 0.6689
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7272 - loss: 0.6692
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7271 - loss: 0.6694
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7271 - loss: 0.6695
195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7270 - loss: 0.6697
200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7270 - loss: 0.6698
205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7269 - loss: 0.6699
210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7269 - loss: 0.6701
215/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7268 - loss: 0.6702
220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7267 - loss: 0.6704
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7266 - loss: 0.6706
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7266 - loss: 0.6708
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7265 - loss: 0.6710
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7264 - loss: 0.6712
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7263 - loss: 0.6714
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7262 - loss: 0.6715
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7261 - loss: 0.6717
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7261 - loss: 0.6718
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7260 - loss: 0.6719
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7260 - loss: 0.6720
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7259 - loss: 0.6722
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7258 - loss: 0.6723
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7257 - loss: 0.6724
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7257 - loss: 0.6726
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7256 - loss: 0.6727
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7255 - loss: 0.6729
303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7254 - loss: 0.6731
308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7253 - loss: 0.6732
313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7252 - loss: 0.6734
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7251 - loss: 0.6735
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7250 - loss: 0.6736
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7250 - loss: 0.6738
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7249 - loss: 0.6739
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7248 - loss: 0.6740
343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7248 - loss: 0.6741
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7247 - loss: 0.6742
353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7246 - loss: 0.6744
358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7246 - loss: 0.6745
363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7245 - loss: 0.6746
368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7245 - loss: 0.6747
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7244 - loss: 0.6748
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7243 - loss: 0.6749
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7243 - loss: 0.6749
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7242 - loss: 0.6750
390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7242 - loss: 0.6751
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7241 - loss: 0.6752
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7240 - loss: 0.6752
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7240 - loss: 0.6753
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7239 - loss: 0.6754
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7238 - loss: 0.6755
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7238 - loss: 0.6755
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6756
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6756
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6757
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6757
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7235 - loss: 0.6758
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7235 - loss: 0.6758
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7234 - loss: 0.6759
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7234 - loss: 0.6759
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7233 - loss: 0.6760
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7232 - loss: 0.6761
Epoch 28: val_accuracy did not improve from 0.74844
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7232 - loss: 0.6761 - val_accuracy: 0.7470 - val_loss: 0.6271 - learning_rate: 2.0000e-04
Epoch 29/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.6250 - loss: 0.7499
5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.7010 - loss: 0.6746
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7146 - loss: 0.6684
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7221 - loss: 0.6584
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7270 - loss: 0.6523
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7275 - loss: 0.6511
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7274 - loss: 0.6519
34/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7280 - loss: 0.6526
37/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7284 - loss: 0.6530
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7293 - loss: 0.6528
46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7301 - loss: 0.6526
51/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7307 - loss: 0.6527
56/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7312 - loss: 0.6529
61/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7316 - loss: 0.6532
66/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7316 - loss: 0.6537
71/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7318 - loss: 0.6537
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7317 - loss: 0.6539
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7312 - loss: 0.6549
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Epoch 29: val_accuracy improved from 0.74844 to 0.75166, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7234 - loss: 0.6711 - val_accuracy: 0.7517 - val_loss: 0.6202 - learning_rate: 2.0000e-04
Epoch 30/45
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195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7254 - loss: 0.6674
200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7253 - loss: 0.6676
205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7253 - loss: 0.6677
210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7252 - loss: 0.6679
215/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7251 - loss: 0.6681
220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7250 - loss: 0.6683
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7249 - loss: 0.6685
230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7247 - loss: 0.6687
235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7246 - loss: 0.6689
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7245 - loss: 0.6691
245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7244 - loss: 0.6693
250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7243 - loss: 0.6694
255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7243 - loss: 0.6695
260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7242 - loss: 0.6696
265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7241 - loss: 0.6697
270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7241 - loss: 0.6698
274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7240 - loss: 0.6698
279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7240 - loss: 0.6699
284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7239 - loss: 0.6699
289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7239 - loss: 0.6700
294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7238 - loss: 0.6700
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7238 - loss: 0.6701
303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7238 - loss: 0.6702
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7237 - loss: 0.6702
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7237 - loss: 0.6703
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7237 - loss: 0.6704
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6704
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6705
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6705
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6705
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6705
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6706
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6706
353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6706
358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6706
363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6707
368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6707
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6707
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6708
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6708
388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7235 - loss: 0.6708
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7235 - loss: 0.6708
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6708
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6708
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6708
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6708
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6708
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6708
Epoch 30: val_accuracy did not improve from 0.75166
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7237 - loss: 0.6708 - val_accuracy: 0.7402 - val_loss: 0.6328 - learning_rate: 2.0000e-04
Epoch 31/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:09 147ms/step - accuracy: 0.8438 - loss: 0.5112
4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.8118 - loss: 0.5421
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.7935 - loss: 0.5802
11/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.7715 - loss: 0.6157
16/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.7552 - loss: 0.6465
21/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7457 - loss: 0.6638
26/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7390 - loss: 0.6742
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7346 - loss: 0.6803
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7323 - loss: 0.6833
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7301 - loss: 0.6863
46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7283 - loss: 0.6896
51/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7269 - loss: 0.6925
56/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7256 - loss: 0.6951
61/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7244 - loss: 0.6968
66/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7236 - loss: 0.6977
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7230 - loss: 0.6983
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7225 - loss: 0.6985
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7222 - loss: 0.6986
86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7219 - loss: 0.6985
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7216 - loss: 0.6984
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7214 - loss: 0.6984
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7212 - loss: 0.6984
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7210 - loss: 0.6983
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7209 - loss: 0.6981
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7209 - loss: 0.6980
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7208 - loss: 0.6978
126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7207 - loss: 0.6977
131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7206 - loss: 0.6976
136/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7205 - loss: 0.6974
141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7204 - loss: 0.6973
146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7202 - loss: 0.6973
151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7200 - loss: 0.6973
156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7199 - loss: 0.6974
161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7197 - loss: 0.6974
166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7196 - loss: 0.6973
171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7195 - loss: 0.6973
176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7194 - loss: 0.6972
181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7193 - loss: 0.6971
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7193 - loss: 0.6970
191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7193 - loss: 0.6968
196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7194 - loss: 0.6966
201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7194 - loss: 0.6963
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7195 - loss: 0.6961
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7195 - loss: 0.6958
216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7196 - loss: 0.6956
221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7197 - loss: 0.6953
226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7198 - loss: 0.6951
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7198 - loss: 0.6948
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7199 - loss: 0.6946
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7199 - loss: 0.6944
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7199 - loss: 0.6941
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7200 - loss: 0.6939
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7200 - loss: 0.6937
261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7201 - loss: 0.6935
266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7202 - loss: 0.6932
271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7202 - loss: 0.6930
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7203 - loss: 0.6927
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7204 - loss: 0.6925
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7204 - loss: 0.6922
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7205 - loss: 0.6920
295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7205 - loss: 0.6919
299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7205 - loss: 0.6918
303/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7205 - loss: 0.6916
308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7206 - loss: 0.6915
313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7206 - loss: 0.6914
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7206 - loss: 0.6912
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7206 - loss: 0.6911
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7206 - loss: 0.6910
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7206 - loss: 0.6909
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6908
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6907
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6906
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6905
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6903
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6902
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6901
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6900
367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6899
372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7208 - loss: 0.6898
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6896
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6895
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6894
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6893
397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6891
402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6890
407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6889
412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6887
417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6886
422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6885
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6884
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6883
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6881
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6880
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7209 - loss: 0.6879
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7209 - loss: 0.6878
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7209 - loss: 0.6877
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7209 - loss: 0.6876
467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7209 - loss: 0.6875
Epoch 31: val_accuracy did not improve from 0.75166
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7209 - loss: 0.6874 - val_accuracy: 0.7402 - val_loss: 0.6422 - learning_rate: 2.0000e-04
Epoch 32/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 52s 112ms/step - accuracy: 0.8125 - loss: 0.6429
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8084 - loss: 0.5991
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7823 - loss: 0.6129
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7710 - loss: 0.6174
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7673 - loss: 0.6163
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7635 - loss: 0.6186
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7610 - loss: 0.6199
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7591 - loss: 0.6217
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7580 - loss: 0.6223
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7575 - loss: 0.6225
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7572 - loss: 0.6229
54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7569 - loss: 0.6234
59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7564 - loss: 0.6239
64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7559 - loss: 0.6244
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7555 - loss: 0.6247
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7551 - loss: 0.6251
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7546 - loss: 0.6256
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7540 - loss: 0.6266
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7533 - loss: 0.6276
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7525 - loss: 0.6287
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7516 - loss: 0.6299
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7508 - loss: 0.6312
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7501 - loss: 0.6323
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7495 - loss: 0.6333
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7489 - loss: 0.6343
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7484 - loss: 0.6352
129/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7479 - loss: 0.6360
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7474 - loss: 0.6367
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7470 - loss: 0.6373
143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7467 - loss: 0.6378
148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7463 - loss: 0.6384
153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7458 - loss: 0.6390
158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7454 - loss: 0.6396
163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7450 - loss: 0.6401
168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7447 - loss: 0.6406
173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7443 - loss: 0.6410
178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7440 - loss: 0.6414
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7437 - loss: 0.6418
187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7435 - loss: 0.6420
192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7432 - loss: 0.6423
197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7430 - loss: 0.6426
202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7428 - loss: 0.6430
207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7425 - loss: 0.6433
212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7423 - loss: 0.6436
217/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7421 - loss: 0.6439
222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7419 - loss: 0.6443
227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7417 - loss: 0.6445
232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7415 - loss: 0.6448
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7414 - loss: 0.6451
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7412 - loss: 0.6454
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7410 - loss: 0.6458
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7408 - loss: 0.6461
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7407 - loss: 0.6464
261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7405 - loss: 0.6468
266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7403 - loss: 0.6471
271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7400 - loss: 0.6475
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7398 - loss: 0.6478
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7396 - loss: 0.6482
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7394 - loss: 0.6485
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7392 - loss: 0.6489
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7390 - loss: 0.6493
301/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7389 - loss: 0.6496
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7387 - loss: 0.6500
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7385 - loss: 0.6503
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7383 - loss: 0.6507
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7381 - loss: 0.6510
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7380 - loss: 0.6514
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6517
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7376 - loss: 0.6520
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7375 - loss: 0.6523
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6525
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7372 - loss: 0.6528
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7371 - loss: 0.6530
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7369 - loss: 0.6533
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7368 - loss: 0.6535
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7367 - loss: 0.6537
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7365 - loss: 0.6539
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7364 - loss: 0.6542
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7363 - loss: 0.6544
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7361 - loss: 0.6547
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7360 - loss: 0.6549
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7359 - loss: 0.6551
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7357 - loss: 0.6554
411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7356 - loss: 0.6556
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7355 - loss: 0.6558
421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7354 - loss: 0.6560
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7353 - loss: 0.6562
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7352 - loss: 0.6564
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7350 - loss: 0.6566
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7349 - loss: 0.6567
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7348 - loss: 0.6569
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7347 - loss: 0.6571
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7346 - loss: 0.6573
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7345 - loss: 0.6574
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7345 - loss: 0.6576
Epoch 32: val_accuracy did not improve from 0.75166
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7343 - loss: 0.6578 - val_accuracy: 0.7402 - val_loss: 0.6478 - learning_rate: 2.0000e-04
Epoch 33/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.6875 - loss: 0.6361
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6595 - loss: 0.7662
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6544 - loss: 0.7604
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6579 - loss: 0.7529
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6648 - loss: 0.7410
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6702 - loss: 0.7317
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6740 - loss: 0.7243
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6776 - loss: 0.7193
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6817 - loss: 0.7140
45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6850 - loss: 0.7098
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6879 - loss: 0.7065
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6903 - loss: 0.7037
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6921 - loss: 0.7011
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6936 - loss: 0.6987
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6950 - loss: 0.6963
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6960 - loss: 0.6949
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6970 - loss: 0.6934
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6980 - loss: 0.6918
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6990 - loss: 0.6904
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6999 - loss: 0.6892
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7007 - loss: 0.6881
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7015 - loss: 0.6872
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7022 - loss: 0.6864
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7028 - loss: 0.6857
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7034 - loss: 0.6851
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7040 - loss: 0.6844
130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7046 - loss: 0.6838
135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7051 - loss: 0.6833
140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7055 - loss: 0.6828
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7059 - loss: 0.6824
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7062 - loss: 0.6822
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7066 - loss: 0.6819
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7070 - loss: 0.6817
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7073 - loss: 0.6815
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7076 - loss: 0.6813
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7079 - loss: 0.6811
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7082 - loss: 0.6810
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7084 - loss: 0.6809
188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7086 - loss: 0.6808
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7088 - loss: 0.6807
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7090 - loss: 0.6807
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7092 - loss: 0.6806
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.6805
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7096 - loss: 0.6804
218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7098 - loss: 0.6803
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7100 - loss: 0.6801
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7102 - loss: 0.6800
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7104 - loss: 0.6799
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7105 - loss: 0.6799
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7107 - loss: 0.6798
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7109 - loss: 0.6797
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7110 - loss: 0.6795
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7112 - loss: 0.6794
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7114 - loss: 0.6793
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7116 - loss: 0.6792
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7117 - loss: 0.6790
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7119 - loss: 0.6789
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7121 - loss: 0.6788
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7123 - loss: 0.6787
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7125 - loss: 0.6785
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7126 - loss: 0.6784
303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7128 - loss: 0.6783
308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7130 - loss: 0.6782
313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7131 - loss: 0.6781
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7133 - loss: 0.6780
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7134 - loss: 0.6779
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7136 - loss: 0.6778
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7137 - loss: 0.6777
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7139 - loss: 0.6776
343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7140 - loss: 0.6775
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7142 - loss: 0.6774
353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7143 - loss: 0.6773
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7144 - loss: 0.6772
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7145 - loss: 0.6771
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7147 - loss: 0.6770
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7148 - loss: 0.6769
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7149 - loss: 0.6768
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7151 - loss: 0.6766
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7152 - loss: 0.6766
389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7153 - loss: 0.6765
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7154 - loss: 0.6764
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7155 - loss: 0.6763
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7156 - loss: 0.6762
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7157 - loss: 0.6761
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7158 - loss: 0.6760
417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7159 - loss: 0.6759
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7160 - loss: 0.6758
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7160 - loss: 0.6758
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7161 - loss: 0.6757
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7162 - loss: 0.6757
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7163 - loss: 0.6756
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7164 - loss: 0.6754
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7165 - loss: 0.6753
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7166 - loss: 0.6752
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7167 - loss: 0.6751
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7168 - loss: 0.6750
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7169 - loss: 0.6749
471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7171 - loss: 0.6748
Epoch 33: val_accuracy did not improve from 0.75166
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7172 - loss: 0.6747 - val_accuracy: 0.7468 - val_loss: 0.6348 - learning_rate: 2.0000e-04
Epoch 34/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 104ms/step - accuracy: 0.7812 - loss: 0.4825
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7055 - loss: 0.6792
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7106 - loss: 0.6819
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7215 - loss: 0.6709
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7303 - loss: 0.6605
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7339 - loss: 0.6563
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7350 - loss: 0.6544
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7358 - loss: 0.6540
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7360 - loss: 0.6533
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7355 - loss: 0.6536
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7353 - loss: 0.6536
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7352 - loss: 0.6540
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7349 - loss: 0.6547
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7346 - loss: 0.6554
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7346 - loss: 0.6553
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7346 - loss: 0.6551
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7344 - loss: 0.6549
86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7344 - loss: 0.6545
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7344 - loss: 0.6540
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7346 - loss: 0.6533
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7347 - loss: 0.6526
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7348 - loss: 0.6521
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7348 - loss: 0.6515
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7348 - loss: 0.6511
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7346 - loss: 0.6509
126/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7344 - loss: 0.6508
131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7342 - loss: 0.6508
136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7340 - loss: 0.6507
141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7339 - loss: 0.6507
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7338 - loss: 0.6507
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7337 - loss: 0.6507
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7336 - loss: 0.6507
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7335 - loss: 0.6506
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7334 - loss: 0.6505
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7333 - loss: 0.6505
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7332 - loss: 0.6506
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7332 - loss: 0.6506
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7330 - loss: 0.6507
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7330 - loss: 0.6507
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7329 - loss: 0.6508
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7328 - loss: 0.6508
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7326 - loss: 0.6509
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7325 - loss: 0.6510
214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7324 - loss: 0.6511
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7323 - loss: 0.6513
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7321 - loss: 0.6514
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7320 - loss: 0.6515
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7319 - loss: 0.6516
237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7318 - loss: 0.6516
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7317 - loss: 0.6517
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7316 - loss: 0.6517
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7316 - loss: 0.6518
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7315 - loss: 0.6518
260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7314 - loss: 0.6519
265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7313 - loss: 0.6520
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7313 - loss: 0.6520
274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7312 - loss: 0.6521
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7311 - loss: 0.6522
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7310 - loss: 0.6523
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7310 - loss: 0.6524
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7309 - loss: 0.6525
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7309 - loss: 0.6526
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7308 - loss: 0.6527
305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6528
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6529
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7306 - loss: 0.6531
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7306 - loss: 0.6532
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7305 - loss: 0.6533
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7305 - loss: 0.6534
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7304 - loss: 0.6536
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7303 - loss: 0.6537
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7303 - loss: 0.6538
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7302 - loss: 0.6540
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7302 - loss: 0.6541
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7301 - loss: 0.6543
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6544
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6545
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7299 - loss: 0.6546
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7299 - loss: 0.6547
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7298 - loss: 0.6549
389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7298 - loss: 0.6550
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7297 - loss: 0.6551
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7297 - loss: 0.6552
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7297 - loss: 0.6553
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7296 - loss: 0.6554
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7295 - loss: 0.6556
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7295 - loss: 0.6557
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7294 - loss: 0.6558
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7294 - loss: 0.6559
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7293 - loss: 0.6561
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7292 - loss: 0.6562
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7292 - loss: 0.6563
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7291 - loss: 0.6565
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7291 - loss: 0.6566
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7290 - loss: 0.6568
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7289 - loss: 0.6569
467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7289 - loss: 0.6570
Epoch 34: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.
Epoch 34: val_accuracy did not improve from 0.75166
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7288 - loss: 0.6572 - val_accuracy: 0.7364 - val_loss: 0.6530 - learning_rate: 2.0000e-04
Epoch 35/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.7188 - loss: 0.6853
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6864 - loss: 0.7151
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7080 - loss: 0.6850
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7169 - loss: 0.6678
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7186 - loss: 0.6629
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7206 - loss: 0.6591
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7223 - loss: 0.6571
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7236 - loss: 0.6549
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7247 - loss: 0.6533
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7254 - loss: 0.6527
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7262 - loss: 0.6521
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7267 - loss: 0.6520
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7275 - loss: 0.6521
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7279 - loss: 0.6527
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7280 - loss: 0.6536
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7279 - loss: 0.6547
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7280 - loss: 0.6554
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7281 - loss: 0.6559
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7284 - loss: 0.6562
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7287 - loss: 0.6563
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7289 - loss: 0.6565
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7290 - loss: 0.6568
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7291 - loss: 0.6571
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7291 - loss: 0.6573
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7292 - loss: 0.6575
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7292 - loss: 0.6578
129/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7292 - loss: 0.6581
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7291 - loss: 0.6586
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7291 - loss: 0.6590
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7292 - loss: 0.6594
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7293 - loss: 0.6597
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7294 - loss: 0.6598
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7295 - loss: 0.6600
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7295 - loss: 0.6602
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7296 - loss: 0.6602
173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7296 - loss: 0.6603
177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7296 - loss: 0.6603
182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7296 - loss: 0.6604
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7296 - loss: 0.6605
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7296 - loss: 0.6607
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Epoch 35: val_accuracy improved from 0.75166 to 0.75829, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7280 - loss: 0.6650 - val_accuracy: 0.7583 - val_loss: 0.6128 - learning_rate: 4.0000e-05
Epoch 36/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.7812 - loss: 0.4937
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313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7299 - loss: 0.6494
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6496
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6497
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6498
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6499
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6500
343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6501
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6502
353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6503
358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7301 - loss: 0.6504
363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7301 - loss: 0.6505
368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7301 - loss: 0.6505
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7301 - loss: 0.6506
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7301 - loss: 0.6507
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7302 - loss: 0.6507
388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7302 - loss: 0.6507
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7302 - loss: 0.6508
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7303 - loss: 0.6508
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7303 - loss: 0.6509
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7303 - loss: 0.6509
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7303 - loss: 0.6509
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7303 - loss: 0.6510
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7304 - loss: 0.6510
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7304 - loss: 0.6511
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7304 - loss: 0.6511
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7304 - loss: 0.6512
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7304 - loss: 0.6512
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7304 - loss: 0.6512
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7305 - loss: 0.6513
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7305 - loss: 0.6513
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7305 - loss: 0.6513
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7305 - loss: 0.6514
Epoch 36: val_accuracy did not improve from 0.75829
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7305 - loss: 0.6514 - val_accuracy: 0.7553 - val_loss: 0.6146 - learning_rate: 4.0000e-05
Epoch 37/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 56s 120ms/step - accuracy: 0.7500 - loss: 0.5479
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6908 - loss: 0.6815
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6932 - loss: 0.6756
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6996 - loss: 0.6636
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7045 - loss: 0.6562
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7089 - loss: 0.6501
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7124 - loss: 0.6490
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7138 - loss: 0.6498
40/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7146 - loss: 0.6511
45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7153 - loss: 0.6524
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7163 - loss: 0.6528
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7169 - loss: 0.6530
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7175 - loss: 0.6531
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7180 - loss: 0.6531
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7185 - loss: 0.6532
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7190 - loss: 0.6532
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7196 - loss: 0.6531
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7202 - loss: 0.6531
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7207 - loss: 0.6533
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7211 - loss: 0.6536
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7215 - loss: 0.6540
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7219 - loss: 0.6542
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7222 - loss: 0.6543
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7226 - loss: 0.6543
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7229 - loss: 0.6543
125/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7232 - loss: 0.6545
130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7235 - loss: 0.6546
135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7238 - loss: 0.6547
140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7240 - loss: 0.6548
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7243 - loss: 0.6549
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7245 - loss: 0.6549
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7246 - loss: 0.6550
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7247 - loss: 0.6551
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7248 - loss: 0.6552
170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7249 - loss: 0.6554
175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7250 - loss: 0.6556
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7251 - loss: 0.6557
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7252 - loss: 0.6559
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7254 - loss: 0.6559
195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7255 - loss: 0.6560
200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7256 - loss: 0.6562
205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7257 - loss: 0.6563
210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7258 - loss: 0.6564
215/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7259 - loss: 0.6566
220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7260 - loss: 0.6567
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7261 - loss: 0.6568
230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7261 - loss: 0.6570
235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7262 - loss: 0.6571
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7263 - loss: 0.6572
245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7263 - loss: 0.6572
250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7264 - loss: 0.6572
255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7265 - loss: 0.6572
260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7266 - loss: 0.6572
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7267 - loss: 0.6573
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7267 - loss: 0.6573
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7268 - loss: 0.6574
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7268 - loss: 0.6574
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7268 - loss: 0.6575
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7269 - loss: 0.6575
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7269 - loss: 0.6575
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7270 - loss: 0.6575
301/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7270 - loss: 0.6575
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7271 - loss: 0.6575
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7272 - loss: 0.6574
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7272 - loss: 0.6574
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7273 - loss: 0.6574
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7274 - loss: 0.6573
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7275 - loss: 0.6573
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7275 - loss: 0.6573
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7276 - loss: 0.6572
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7277 - loss: 0.6572
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7278 - loss: 0.6571
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7278 - loss: 0.6571
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7279 - loss: 0.6570
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7280 - loss: 0.6570
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7281 - loss: 0.6570
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7281 - loss: 0.6569
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7282 - loss: 0.6569
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7282 - loss: 0.6569
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7283 - loss: 0.6569
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7284 - loss: 0.6569
397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7285 - loss: 0.6569
402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7285 - loss: 0.6569
407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7286 - loss: 0.6568
412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7287 - loss: 0.6568
417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7288 - loss: 0.6568
422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7288 - loss: 0.6568
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7289 - loss: 0.6568
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7290 - loss: 0.6567
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7291 - loss: 0.6567
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7291 - loss: 0.6567
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7292 - loss: 0.6566
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7293 - loss: 0.6566
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7294 - loss: 0.6565
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7294 - loss: 0.6565
467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7295 - loss: 0.6564
Epoch 37: val_accuracy did not improve from 0.75829
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7296 - loss: 0.6564 - val_accuracy: 0.7549 - val_loss: 0.6183 - learning_rate: 4.0000e-05
Epoch 38/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 106ms/step - accuracy: 0.6875 - loss: 0.6545
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7509 - loss: 0.6073
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7607 - loss: 0.6014
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7620 - loss: 0.5998
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7638 - loss: 0.5966
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7639 - loss: 0.5943
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7640 - loss: 0.5927
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7630 - loss: 0.5938
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7622 - loss: 0.5950
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7614 - loss: 0.5963
50/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7608 - loss: 0.5972
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7598 - loss: 0.5987
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7591 - loss: 0.5998
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7584 - loss: 0.6010
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7575 - loss: 0.6026
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7565 - loss: 0.6043
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7556 - loss: 0.6058
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7547 - loss: 0.6071
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7540 - loss: 0.6081
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7532 - loss: 0.6091
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7523 - loss: 0.6102
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7514 - loss: 0.6113
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7506 - loss: 0.6124
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7498 - loss: 0.6136
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7491 - loss: 0.6148
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7484 - loss: 0.6159
130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7478 - loss: 0.6170
135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7472 - loss: 0.6180
140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7467 - loss: 0.6189
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7461 - loss: 0.6198
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7456 - loss: 0.6209
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7451 - loss: 0.6219
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7446 - loss: 0.6228
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7442 - loss: 0.6238
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7438 - loss: 0.6245
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7434 - loss: 0.6255
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7430 - loss: 0.6264
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7427 - loss: 0.6271
188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7423 - loss: 0.6279
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7420 - loss: 0.6286
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7418 - loss: 0.6293
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7415 - loss: 0.6299
207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7413 - loss: 0.6304
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7411 - loss: 0.6309
216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7408 - loss: 0.6315
221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7406 - loss: 0.6321
226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7403 - loss: 0.6327
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7401 - loss: 0.6332
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7399 - loss: 0.6337
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7397 - loss: 0.6342
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7395 - loss: 0.6346
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7394 - loss: 0.6350
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7392 - loss: 0.6353
261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7391 - loss: 0.6357
266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7390 - loss: 0.6360
271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7388 - loss: 0.6363
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7387 - loss: 0.6366
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7386 - loss: 0.6370
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7385 - loss: 0.6372
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7383 - loss: 0.6376
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7382 - loss: 0.6380
302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7381 - loss: 0.6384
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7380 - loss: 0.6386
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7380 - loss: 0.6389
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7379 - loss: 0.6392
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6395
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7377 - loss: 0.6397
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7376 - loss: 0.6400
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7376 - loss: 0.6402
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7375 - loss: 0.6404
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7375 - loss: 0.6406
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7374 - loss: 0.6408
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7374 - loss: 0.6409
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6411
367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6412
372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6414
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7372 - loss: 0.6415
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7372 - loss: 0.6417
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7371 - loss: 0.6419
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7371 - loss: 0.6420
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7371 - loss: 0.6422
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6423
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6424
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6425
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6427
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6428
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6429
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6430
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6431
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6432
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6432
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6433
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6434
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6435
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6436
471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6437
Epoch 38: val_accuracy did not improve from 0.75829
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7368 - loss: 0.6438 - val_accuracy: 0.7563 - val_loss: 0.6083 - learning_rate: 4.0000e-05
Epoch 39/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 52s 111ms/step - accuracy: 0.7812 - loss: 0.5779
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8012 - loss: 0.5409
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7870 - loss: 0.5563
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7816 - loss: 0.5612
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7780 - loss: 0.5658
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7748 - loss: 0.5722
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7726 - loss: 0.5755
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7708 - loss: 0.5775
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7690 - loss: 0.5809
45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7669 - loss: 0.5847
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7653 - loss: 0.5876
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7635 - loss: 0.5909
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7617 - loss: 0.5943
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7601 - loss: 0.5975
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7587 - loss: 0.5999
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7576 - loss: 0.6018
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7566 - loss: 0.6033
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7555 - loss: 0.6049
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7543 - loss: 0.6065
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7533 - loss: 0.6081
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7525 - loss: 0.6095
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7518 - loss: 0.6105
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7513 - loss: 0.6114
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7508 - loss: 0.6121
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7504 - loss: 0.6128
125/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7499 - loss: 0.6135
130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7496 - loss: 0.6140
135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7492 - loss: 0.6144
140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7489 - loss: 0.6148
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7486 - loss: 0.6154
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7482 - loss: 0.6160
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7479 - loss: 0.6166
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7476 - loss: 0.6172
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7473 - loss: 0.6177
170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7471 - loss: 0.6181
175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7468 - loss: 0.6186
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7466 - loss: 0.6190
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7463 - loss: 0.6195
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7461 - loss: 0.6199
195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7459 - loss: 0.6202
200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7458 - loss: 0.6206
205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7456 - loss: 0.6209
210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7455 - loss: 0.6212
215/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7453 - loss: 0.6215
220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7452 - loss: 0.6218
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7450 - loss: 0.6221
230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7449 - loss: 0.6224
235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7448 - loss: 0.6227
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7447 - loss: 0.6230
245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7447 - loss: 0.6232
250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7446 - loss: 0.6234
255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7446 - loss: 0.6236
260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7445 - loss: 0.6237
265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7445 - loss: 0.6239
270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7444 - loss: 0.6241
275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7443 - loss: 0.6243
280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7443 - loss: 0.6245
285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7442 - loss: 0.6247
290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7441 - loss: 0.6249
295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7440 - loss: 0.6252
300/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7439 - loss: 0.6254
305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7438 - loss: 0.6256
310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7437 - loss: 0.6258
315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7436 - loss: 0.6260
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7435 - loss: 0.6262
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7434 - loss: 0.6265
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7433 - loss: 0.6267
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7432 - loss: 0.6269
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7431 - loss: 0.6271
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7430 - loss: 0.6273
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7429 - loss: 0.6275
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7428 - loss: 0.6276
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7428 - loss: 0.6278
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7427 - loss: 0.6280
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7426 - loss: 0.6281
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7425 - loss: 0.6283
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7425 - loss: 0.6284
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7424 - loss: 0.6285
389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7423 - loss: 0.6287
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7422 - loss: 0.6288
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7421 - loss: 0.6290
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7421 - loss: 0.6292
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7420 - loss: 0.6293
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7419 - loss: 0.6294
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7419 - loss: 0.6296
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7418 - loss: 0.6297
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7417 - loss: 0.6299
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7417 - loss: 0.6300
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7416 - loss: 0.6302
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7416 - loss: 0.6303
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7415 - loss: 0.6305
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7414 - loss: 0.6307
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7414 - loss: 0.6308
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7413 - loss: 0.6310
471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7412 - loss: 0.6312
Epoch 39: val_accuracy did not improve from 0.75829
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7412 - loss: 0.6313 - val_accuracy: 0.7575 - val_loss: 0.6137 - learning_rate: 4.0000e-05
Epoch 40/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:01 130ms/step - accuracy: 0.5938 - loss: 0.9132
5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.6492 - loss: 0.7829
9/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6754 - loss: 0.7277
13/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.6891 - loss: 0.7007
16/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6963 - loss: 0.6902
21/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7061 - loss: 0.6784
26/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7122 - loss: 0.6708
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7165 - loss: 0.6642
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7191 - loss: 0.6595
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7215 - loss: 0.6553
45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7239 - loss: 0.6513
50/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7255 - loss: 0.6487
55/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7269 - loss: 0.6471
61/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7285 - loss: 0.6453
66/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7296 - loss: 0.6436
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7306 - loss: 0.6420
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7316 - loss: 0.6407
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7324 - loss: 0.6395
86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7330 - loss: 0.6385
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7336 - loss: 0.6376
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7339 - loss: 0.6370
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7343 - loss: 0.6367
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7346 - loss: 0.6362
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7350 - loss: 0.6356
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7353 - loss: 0.6351
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7356 - loss: 0.6346
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7358 - loss: 0.6343
129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7360 - loss: 0.6341
134/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7362 - loss: 0.6338
139/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7364 - loss: 0.6337
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7365 - loss: 0.6335
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7366 - loss: 0.6334
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7368 - loss: 0.6333
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7369 - loss: 0.6332
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7370 - loss: 0.6332
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7370 - loss: 0.6332
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7370 - loss: 0.6333
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7370 - loss: 0.6335
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7370 - loss: 0.6336
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7371 - loss: 0.6337
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7371 - loss: 0.6337
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7371 - loss: 0.6338
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7372 - loss: 0.6339
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7372 - loss: 0.6339
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7372 - loss: 0.6340
217/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7373 - loss: 0.6341
222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7373 - loss: 0.6341
227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7374 - loss: 0.6342
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7374 - loss: 0.6342
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7375 - loss: 0.6343
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7375 - loss: 0.6343
245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7375 - loss: 0.6343
250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7376 - loss: 0.6344
255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7376 - loss: 0.6345
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7376 - loss: 0.6346
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7377 - loss: 0.6347
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7377 - loss: 0.6348
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7377 - loss: 0.6349
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7377 - loss: 0.6350
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7378 - loss: 0.6351
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7378 - loss: 0.6352
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7378 - loss: 0.6353
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7378 - loss: 0.6355
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7378 - loss: 0.6356
305/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7378 - loss: 0.6358
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6359
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6360
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6361
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6363
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6364
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7377 - loss: 0.6365
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7377 - loss: 0.6367
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7376 - loss: 0.6368
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7376 - loss: 0.6369
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7376 - loss: 0.6371
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7375 - loss: 0.6372
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7375 - loss: 0.6373
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7374 - loss: 0.6374
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7374 - loss: 0.6375
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6376
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6377
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6378
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7372 - loss: 0.6379
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7372 - loss: 0.6380
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7372 - loss: 0.6380
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7371 - loss: 0.6381
412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7371 - loss: 0.6382
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7371 - loss: 0.6383
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6383
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6384
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6385
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6386
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6387
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6387
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6388
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6389
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6390
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6391
471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7367 - loss: 0.6392
Epoch 40: val_accuracy did not improve from 0.75829
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7367 - loss: 0.6393 - val_accuracy: 0.7551 - val_loss: 0.6151 - learning_rate: 4.0000e-05
Epoch 41/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.8125 - loss: 0.5863
4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 17ms/step - accuracy: 0.7682 - loss: 0.6187
9/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7584 - loss: 0.6302
13/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7559 - loss: 0.6265
18/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7536 - loss: 0.6267
23/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7520 - loss: 0.6311
28/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7503 - loss: 0.6375
33/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7492 - loss: 0.6416
38/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7484 - loss: 0.6446
43/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7473 - loss: 0.6473
48/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7464 - loss: 0.6488
54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7454 - loss: 0.6501
59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7444 - loss: 0.6517
64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7433 - loss: 0.6532
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7424 - loss: 0.6546
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7416 - loss: 0.6557
78/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7411 - loss: 0.6562
83/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7408 - loss: 0.6567
88/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7405 - loss: 0.6571
93/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7402 - loss: 0.6573
97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7400 - loss: 0.6574
102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7397 - loss: 0.6573
107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7396 - loss: 0.6572
112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7395 - loss: 0.6570
117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7395 - loss: 0.6567
122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7396 - loss: 0.6563
127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7397 - loss: 0.6560
132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7398 - loss: 0.6558
136/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7398 - loss: 0.6556
140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7397 - loss: 0.6555
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7397 - loss: 0.6552
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7397 - loss: 0.6551
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7398 - loss: 0.6548
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7398 - loss: 0.6546
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7399 - loss: 0.6544
170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7399 - loss: 0.6542
175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7400 - loss: 0.6540
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7401 - loss: 0.6538
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7401 - loss: 0.6536
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7402 - loss: 0.6535
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7402 - loss: 0.6534
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7402 - loss: 0.6533
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7403 - loss: 0.6531
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7403 - loss: 0.6530
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7403 - loss: 0.6529
218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7403 - loss: 0.6529
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7403 - loss: 0.6528
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7403 - loss: 0.6527
232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7403 - loss: 0.6527
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7403 - loss: 0.6526
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7403 - loss: 0.6526
245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7402 - loss: 0.6525
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7402 - loss: 0.6524
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7402 - loss: 0.6524
257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7402 - loss: 0.6523
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7402 - loss: 0.6522
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7401 - loss: 0.6521
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7401 - loss: 0.6520
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7401 - loss: 0.6518
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7401 - loss: 0.6518
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7401 - loss: 0.6517
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7401 - loss: 0.6516
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7400 - loss: 0.6515
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7400 - loss: 0.6515
306/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7400 - loss: 0.6515
310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7399 - loss: 0.6515
315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7399 - loss: 0.6515
320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7398 - loss: 0.6515
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7398 - loss: 0.6514
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7398 - loss: 0.6514
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Epoch 41: val_accuracy improved from 0.75829 to 0.75909, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 7s 14ms/step - accuracy: 0.7391 - loss: 0.6507 - val_accuracy: 0.7591 - val_loss: 0.6059 - learning_rate: 4.0000e-05
Epoch 42/45
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459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7372 - loss: 0.6435
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7372 - loss: 0.6435
471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7372 - loss: 0.6434
Epoch 42: val_accuracy did not improve from 0.75909
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7372 - loss: 0.6434 - val_accuracy: 0.7585 - val_loss: 0.6082 - learning_rate: 4.0000e-05
Epoch 43/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.6250 - loss: 0.9868
5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6422 - loss: 0.8508
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6515 - loss: 0.7963
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6572 - loss: 0.7846
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6669 - loss: 0.7685
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6738 - loss: 0.7547
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85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7091 - loss: 0.6896
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95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7115 - loss: 0.6848
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7126 - loss: 0.6825
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136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7183 - loss: 0.6704
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151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7200 - loss: 0.6671
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161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7208 - loss: 0.6654
166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7212 - loss: 0.6647
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218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7234 - loss: 0.6608
222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7235 - loss: 0.6606
227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7236 - loss: 0.6603
232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7237 - loss: 0.6600
237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7238 - loss: 0.6598
242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7239 - loss: 0.6596
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267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7244 - loss: 0.6585
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282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7246 - loss: 0.6581
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306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7248 - loss: 0.6576
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7248 - loss: 0.6575
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7248 - loss: 0.6574
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7249 - loss: 0.6573
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7249 - loss: 0.6572
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7250 - loss: 0.6571
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7251 - loss: 0.6570
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341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7252 - loss: 0.6568
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7252 - loss: 0.6567
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356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7254 - loss: 0.6566
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7254 - loss: 0.6565
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386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7258 - loss: 0.6562
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7258 - loss: 0.6561
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406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7260 - loss: 0.6560
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441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7264 - loss: 0.6558
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7264 - loss: 0.6558
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7264 - loss: 0.6558
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7265 - loss: 0.6558
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7265 - loss: 0.6558
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Epoch 43: val_accuracy improved from 0.75909 to 0.76150, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7266 - loss: 0.6557 - val_accuracy: 0.7615 - val_loss: 0.6053 - learning_rate: 4.0000e-05
Epoch 44/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.7188 - loss: 0.6566
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7488 - loss: 0.5630
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7520 - loss: 0.5588
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7483 - loss: 0.5762
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7471 - loss: 0.5848
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7478 - loss: 0.5872
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7488 - loss: 0.5891
36/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7493 - loss: 0.5907
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Epoch 44: val_accuracy improved from 0.76150 to 0.76211, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7386 - loss: 0.6314 - val_accuracy: 0.7621 - val_loss: 0.6069 - learning_rate: 4.0000e-05
Epoch 45/45
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.5938 - loss: 0.8244
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Epoch 45: val_accuracy did not improve from 0.76211
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7328 - loss: 0.6400 - val_accuracy: 0.7569 - val_loss: 0.6118 - learning_rate: 4.0000e-05
Restoring model weights from the end of the best epoch: 43.
Plotting the Training and Validation Accuracies¶
plt.plot(history_2.history["accuracy"])
plt.plot(history_2.history["val_accuracy"])
plt.title("CNN Model 2 accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train", "validation"], loc="upper left")
plt.show()
Evaluating the Model on the Test Set¶
# Calculate the number of steps for the entire test set to be processed
test_steps = test_generator.samples // batch_size
# If the number of samples isn't a multiple of the batch size,
# you have one more batch with the remaining samples
if test_generator.samples % batch_size > 0:
test_steps += 1
# Evaluating the model on the test set
evaluation_results = model_2.evaluate(test_generator, steps=test_steps)
print(f"Loss: {evaluation_results[0]}, Accuracy: {evaluation_results[1]}")
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.8125 - loss: 0.4481
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7875 - loss: 0.5252
Loss: 0.5420138835906982, Accuracy: 0.7890625
Plotting Confusion Matrix¶
pred_probabilities = model_2.predict(test_generator, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)
# Getting the true labels from the generator
y_true = test_generator.classes
# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))
# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("CNN Model 2 Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 1s 394ms/step
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step
precision recall f1-score support
happy 0.81 0.81 0.81 32
neutral 0.66 0.78 0.71 32
sad 0.75 0.66 0.70 32
surprise 0.97 0.91 0.94 32
accuracy 0.79 128
macro avg 0.80 0.79 0.79 128
weighted avg 0.80 0.79 0.79 128
Observations and Insights:
- The model's total parameters are 397,860, which includes 396,772 trainable parameters.
- Obtained a 78.90% accuracy on the test set, surpassing the first model's test accuracy.
- Precision and recall improvements are noted in 'neutral' predictions, while 'happy' and 'surprise' maintain strong performance.
- The neutral and 'sad' classes continue to be challenging for the model, with lower comparative metrics.
Think About It:¶
- Did the models have a satisfactory performance? If not, then what are the possible reasons?
- Answer: The models had moderate performance, up to 79%, with room for improvement and challenges in distinguishing between certain emotions, particularly 'sad' and 'neutral'.
- Which Color mode showed better overall performance? What are the possible reasons? Do you think having 'rgb' color mode is needed because the images are already black and white?
- Answer: Grayscale mode yielded sufficient performance given the nature of the data, and using 'rgb' color mode for grayscale images did not provide additional benefits as the color information does not contribute to emotion recognition in this context.
Transfer Learning Architectures¶
In this section, we will create several Transfer Learning architectures. For the pre-trained models, we will select three popular architectures namely, VGG16, ResNet v2, and Efficient Net. The difference between these architectures and the previous architectures is that these will require 3 input channels while the earlier ones worked on 'grayscale' images. Therefore, we need to create new DataLoaders.
Creating our Data Loaders for Transfer Learning Architectures¶
In this section, we are creating data loaders that we will use as inputs to our Neural Network. We will have to go with color_mode = 'rgb' as this is the required format for the transfer learning architectures.
# Set this to 'rgb' as this is the required format for the transfer learning architectures
color_mode = "rgb"
color_layers = 3
# Using the same size as before for the images
img_width, img_height = 48, 48
# A batch size of 32 is appropriate for this dataset provide to provide a good balance
# between the model's ability to generalize (avoid overfitting) and computational efficiency.
batch_size = 32
# Training Data Augmentation for VGG16
train_datagen_vgg16 = ImageDataGenerator(
preprocessing_function=preprocess_input_vgg16, # Use model-specific preprocessing
horizontal_flip=True, # Faces are symmetric; flipping can simulate looking from another direction
brightness_range=(0.5, 1.5), # Randomly adjust brightness to simulate different lighting conditions
shear_range=0.3, # Shear transformations for perspective changes
rotation_range=20, # Slight rotation to introduce variability without distorting emotion features
width_shift_range=0.1, # Slight horizontal shifts to simulate off-center faces
height_shift_range=0.1, # Slight vertical shifts to account for different heights/angles
zoom_range=0.1, # Small zoom in/out to simulate closer or further away faces
)
# Training Data Augmentation for ResNet
train_datagen_resnet = ImageDataGenerator(
preprocessing_function=preprocess_input_resnetv2, # Use model-specific preprocessing
horizontal_flip=True, # Faces are symmetric; flipping can simulate looking from another direction
brightness_range=(0.5, 1.5), # Randomly adjust brightness to simulate different lighting conditions
shear_range=0.3, # Shear transformations for perspective changes
rotation_range=20, # Slight rotation to introduce variability without distorting emotion features
width_shift_range=0.1, # Slight horizontal shifts to simulate off-center faces
height_shift_range=0.1, # Slight vertical shifts to account for different heights/angles
zoom_range=0.1, # Small zoom in/out to simulate closer or further away faces
)
# Training Data Augmentation for EfficientNet
train_datagen_efficientnet = ImageDataGenerator(
preprocessing_function=preprocess_input_efficientnetv2, # Use model-specific preprocessing
horizontal_flip=True, # Faces are symmetric; flipping can simulate looking from another direction
brightness_range=(0.5, 1.5), # Randomly adjust brightness to simulate different lighting conditions
shear_range=0.3, # Shear transformations for perspective changes
rotation_range=20, # Slight rotation to introduce variability without distorting emotion features
width_shift_range=0.1, # Slight horizontal shifts to simulate off-center faces
height_shift_range=0.1, # Slight vertical shifts to account for different heights/angles
zoom_range=0.1, # Small zoom in/out to simulate closer or further away faces
)
# Validation and Testing Data should not be augmented! VGG16 version
validation_datagen_vgg16 = ImageDataGenerator(
preprocessing_function=preprocess_input_vgg16
) # Use model-specific preprocessing
test_datagen_vgg16 = ImageDataGenerator(
preprocessing_function=preprocess_input_vgg16
) # Use model-specific preprocessing
# Validation and Testing Data should not be augmented! ResNet version
validation_datagen_resnet = ImageDataGenerator(
preprocessing_function=preprocess_input_resnetv2
) # Use model-specific preprocessing
test_datagen_resnet = ImageDataGenerator(
preprocessing_function=preprocess_input_resnetv2
) # Use model-specific preprocessing
# Validation and Testing Data should not be augmented! Efficient Net version
validation_datagen_efficientnet = ImageDataGenerator(
preprocessing_function=preprocess_input_efficientnetv2
) # Use model-specific preprocessing
test_datagen_efficientnet = ImageDataGenerator(
preprocessing_function=preprocess_input_efficientnetv2
) # Use model-specific preprocessing
# Creating train_dir, validation_dir, and test_dir with the structure of DATADIR and SUBDIRS
train_dir = os.path.join(DATADIR, SUBDIRS_DICT["train"])
validation_dir = os.path.join(DATADIR, SUBDIRS_DICT["validation"])
test_dir = os.path.join(DATADIR, SUBDIRS_DICT["test"])
# Train Generator VGG16
train_generator_vgg16 = train_datagen_vgg16.flow_from_directory(
train_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode,
class_mode="categorical",
)
# Train Generator ResNet
train_generator_resnet = train_datagen_resnet.flow_from_directory(
train_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode,
class_mode="categorical",
)
# Train Generator EfficientNet
train_generator_efficientnet = train_datagen_efficientnet.flow_from_directory(
train_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode,
class_mode="categorical",
)
# Validation Generator VGG16
validation_generator_vgg16 = validation_datagen_vgg16.flow_from_directory(
validation_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode,
class_mode="categorical",
shuffle=False, # shuffle=False to keep data in order for evaluation
)
# Validation Generator ResNet
validation_generator_resnet = validation_datagen_resnet.flow_from_directory(
validation_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode,
class_mode="categorical",
shuffle=False, # shuffle=False to keep data in order for evaluation
)
# Validation Generator EfficientNet
validation_generator_efficientnet = validation_datagen_efficientnet.flow_from_directory(
validation_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode,
class_mode="categorical",
shuffle=False, # shuffle=False to keep data in order for evaluation
)
# Testing Generator VGG16
test_generator_vgg16 = test_datagen_vgg16.flow_from_directory(
test_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode,
class_mode="categorical",
shuffle=False, # shuffle=False to keep data in order for testing
)
# Testing Generator ResNet
test_generator_resnet = test_datagen_resnet.flow_from_directory(
test_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode,
class_mode="categorical",
shuffle=False, # shuffle=False to keep data in order for testing
)
# Testing Generator EfficientNet
test_generator_efficientnet = test_datagen_efficientnet.flow_from_directory(
test_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode,
class_mode="categorical",
shuffle=False, # shuffle=False to keep data in order for testing
)
Found 15109 images belonging to 4 classes.
Found 15109 images belonging to 4 classes.
Found 15109 images belonging to 4 classes.
Found 4977 images belonging to 4 classes.
Found 4977 images belonging to 4 classes.
Found 4977 images belonging to 4 classes.
Found 128 images belonging to 4 classes.
Found 128 images belonging to 4 classes.
Found 128 images belonging to 4 classes.
VGG16 Model¶
Importing the VGG16 Architecture¶
backend.clear_session()
# Fixing the seed for random number generators so that we can ensure we receive the same output everytime
np.random.seed(42)
random.seed(42)
tf.random.set_seed(42)
vgg_model = VGG16(weights="imagenet", include_top=False, input_shape=(img_width, img_height, color_layers))
vgg_model.summary()
Model: "vgg16"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ input_layer (InputLayer) │ (None, 48, 48, 3) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block1_conv1 (Conv2D) │ (None, 48, 48, 64) │ 1,792 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block1_conv2 (Conv2D) │ (None, 48, 48, 64) │ 36,928 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block1_pool (MaxPooling2D) │ (None, 24, 24, 64) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block2_conv1 (Conv2D) │ (None, 24, 24, 128) │ 73,856 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block2_conv2 (Conv2D) │ (None, 24, 24, 128) │ 147,584 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block2_pool (MaxPooling2D) │ (None, 12, 12, 128) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block3_conv1 (Conv2D) │ (None, 12, 12, 256) │ 295,168 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block3_conv2 (Conv2D) │ (None, 12, 12, 256) │ 590,080 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block3_conv3 (Conv2D) │ (None, 12, 12, 256) │ 590,080 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block3_pool (MaxPooling2D) │ (None, 6, 6, 256) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block4_conv1 (Conv2D) │ (None, 6, 6, 512) │ 1,180,160 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block4_conv2 (Conv2D) │ (None, 6, 6, 512) │ 2,359,808 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block4_conv3 (Conv2D) │ (None, 6, 6, 512) │ 2,359,808 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block4_pool (MaxPooling2D) │ (None, 3, 3, 512) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block5_conv1 (Conv2D) │ (None, 3, 3, 512) │ 2,359,808 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block5_conv2 (Conv2D) │ (None, 3, 3, 512) │ 2,359,808 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block5_conv3 (Conv2D) │ (None, 3, 3, 512) │ 2,359,808 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ block5_pool (MaxPooling2D) │ (None, 1, 1, 512) │ 0 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 14,714,688 (56.13 MB)
Trainable params: 14,714,688 (56.13 MB)
Non-trainable params: 0 (0.00 B)
Model Building¶
- Import VGG16 upto the layer of your choice and add Fully Connected layers on top of it.
# Define a new model that cuts VGG16 at the 'block3_pool' layer
model_output = vgg_model.get_layer("block3_pool").output
cut_model = Model(inputs=vgg_model.input, outputs=model_output)
for layer in vgg_model.layers:
layer.trainable = False
new_vgg16_model = Sequential()
# Adding the convolutional part of the VGG16 model from above
new_vgg16_model.add(cut_model)
# Reduces each feature map to a single value by averaging all elements
new_vgg16_model.add(GlobalAveragePooling2D())
# Adding full connected layers
new_vgg16_model.add(Dense(512, activation="relu"))
new_vgg16_model.add(Dense(128, activation="relu"))
new_vgg16_model.add(Dense(64))
new_vgg16_model.add(BatchNormalization())
new_vgg16_model.add(ReLU()) # Using ReLU after batch normalization
# Adding output layer
new_vgg16_model.add(Dense(4, activation="softmax"))
# Using RMSprop Optimizer
optimizer = RMSprop(learning_rate=0.001)
Compiling and Training the VGG16 Model¶
new_vgg16_model.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])
new_vgg16_model.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ functional_1 (Functional) │ ? │ 1,735,488 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ global_average_pooling2d │ ? │ 0 (unbuilt) │ │ (GlobalAveragePooling2D) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense (Dense) │ ? │ 0 (unbuilt) │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_1 (Dense) │ ? │ 0 (unbuilt) │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_2 (Dense) │ ? │ 0 (unbuilt) │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization │ ? │ 0 (unbuilt) │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ re_lu (ReLU) │ ? │ 0 (unbuilt) │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_3 (Dense) │ ? │ 0 (unbuilt) │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 1,735,488 (6.62 MB)
Trainable params: 0 (0.00 B)
Non-trainable params: 1,735,488 (6.62 MB)
# Get the current time
current_time = datetime.now().strftime("%Y%m%d-%H%M%S")
# Set up Early Stopping with a patience 7 but acting after at least 20 epochs
delayed_early_stopping = DelayedEarlyStopping(
monitor="val_loss", patience=7, verbose=1, restore_best_weights=True, start_epoch=20
)
# Define the learning rate scheduler callback
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, patience=5, min_lr=0.00001, verbose=1)
# Define the saving the best model callback
mc = ModelCheckpoint(
f"{results_path}/best_model_vgg16_{current_time}.keras",
monitor="val_accuracy",
mode="max",
verbose=1,
save_best_only=True,
)
# Fitting the model with 40 epochs and using validation set
history_vgg = new_vgg16_model.fit(
train_generator_vgg16,
epochs=40,
validation_data=validation_generator_vgg16,
callbacks=[reduce_lr, mc, delayed_early_stopping],
)
Epoch 1/40
/home/iamtxena/sandbox/mit-ai/my_env/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored. self._warn_if_super_not_called()
I0000 00:00:1712794452.789847 1490572 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_1155', 40 bytes spill stores, 40 bytes spill loads I0000 00:00:1712794452.792128 1490580 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_1155', 4 bytes spill stores, 4 bytes spill loads
I0000 00:00:1712794453.017261 1490573 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_473', 8 bytes spill stores, 8 bytes spill loads I0000 00:00:1712794453.113233 1490576 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_1155', 32 bytes spill stores, 32 bytes spill loads
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Epoch 1: val_accuracy improved from -inf to 0.44183, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 17s 29ms/step - accuracy: 0.4447 - loss: 1.2222 - val_accuracy: 0.4418 - val_loss: 1.3317 - learning_rate: 0.0010
Epoch 2/40
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3/473 ━━━━━━━━━━━━━━━━━━━━ 12s 26ms/step - accuracy: 0.5521 - loss: 1.0018
6/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5509 - loss: 1.0254
9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5502 - loss: 1.0342
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Epoch 2: val_accuracy improved from 0.44183 to 0.46373, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5297 - loss: 1.0759 - val_accuracy: 0.4637 - val_loss: 1.5747 - learning_rate: 0.0010
Epoch 3/40
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Epoch 3: val_accuracy improved from 0.46373 to 0.56982, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5495 - loss: 1.0414 - val_accuracy: 0.5698 - val_loss: 1.0627 - learning_rate: 0.0010
Epoch 4/40
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221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5864 - loss: 0.9957
224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5862 - loss: 0.9958
227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5861 - loss: 0.9960
230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5859 - loss: 0.9961
233/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5857 - loss: 0.9962
236/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5856 - loss: 0.9964
239/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5854 - loss: 0.9965
242/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5853 - loss: 0.9966
244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5852 - loss: 0.9967
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5851 - loss: 0.9968
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5850 - loss: 0.9969
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5849 - loss: 0.9970
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5847 - loss: 0.9971
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5846 - loss: 0.9971
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5845 - loss: 0.9972
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5843 - loss: 0.9973
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5842 - loss: 0.9975
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5840 - loss: 0.9976
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5839 - loss: 0.9977
276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5838 - loss: 0.9978
279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5836 - loss: 0.9979
282/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5835 - loss: 0.9981
285/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5833 - loss: 0.9982
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5832 - loss: 0.9983
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5831 - loss: 0.9984
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5829 - loss: 0.9985
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5828 - loss: 0.9985
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5828 - loss: 0.9986
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5826 - loss: 0.9987
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5825 - loss: 0.9988
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5824 - loss: 0.9989
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5823 - loss: 0.9989
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5821 - loss: 0.9990
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5820 - loss: 0.9991
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5819 - loss: 0.9992
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5818 - loss: 0.9993
325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5817 - loss: 0.9994
328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5816 - loss: 0.9995
331/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5815 - loss: 0.9996
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5814 - loss: 0.9997
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5812 - loss: 0.9998
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5811 - loss: 1.0000
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5810 - loss: 1.0001
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5809 - loss: 1.0002
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5808 - loss: 1.0003
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5807 - loss: 1.0004
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5806 - loss: 1.0006
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5805 - loss: 1.0007
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5804 - loss: 1.0008
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5803 - loss: 1.0009
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5802 - loss: 1.0010
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5801 - loss: 1.0012
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5800 - loss: 1.0013
376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5799 - loss: 1.0014
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5798 - loss: 1.0016
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5797 - loss: 1.0016
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5796 - loss: 1.0018
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5795 - loss: 1.0019
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5794 - loss: 1.0020
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5793 - loss: 1.0021
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5792 - loss: 1.0023
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5791 - loss: 1.0023
400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5791 - loss: 1.0024
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5790 - loss: 1.0026
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5789 - loss: 1.0027
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5788 - loss: 1.0028
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5787 - loss: 1.0029
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5786 - loss: 1.0031
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5785 - loss: 1.0032
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5784 - loss: 1.0033
424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5783 - loss: 1.0034
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5782 - loss: 1.0035
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5782 - loss: 1.0037
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5781 - loss: 1.0038
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5780 - loss: 1.0039
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5779 - loss: 1.0040
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5779 - loss: 1.0041
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5778 - loss: 1.0041
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5777 - loss: 1.0042
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5777 - loss: 1.0043
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5776 - loss: 1.0044
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5775 - loss: 1.0045
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5775 - loss: 1.0046
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5774 - loss: 1.0047
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5773 - loss: 1.0047
Epoch 4: val_accuracy did not improve from 0.56982
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5772 - loss: 1.0050 - val_accuracy: 0.4676 - val_loss: 1.1357 - learning_rate: 0.0010
Epoch 5/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:32 197ms/step - accuracy: 0.5000 - loss: 0.9534
4/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5020 - loss: 1.0413
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5090 - loss: 1.0456
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5172 - loss: 1.0408
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5256 - loss: 1.0326
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5298 - loss: 1.0310
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5334 - loss: 1.0299
22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5367 - loss: 1.0294
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5388 - loss: 1.0303
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5414 - loss: 1.0294
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5434 - loss: 1.0285
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5455 - loss: 1.0274
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5486 - loss: 1.0259
38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5510 - loss: 1.0253
41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5527 - loss: 1.0254
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5544 - loss: 1.0250
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5560 - loss: 1.0246
50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5575 - loss: 1.0246
53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5587 - loss: 1.0249
56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5596 - loss: 1.0250
59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5605 - loss: 1.0251
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5612 - loss: 1.0253
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5617 - loss: 1.0255
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5620 - loss: 1.0256
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5624 - loss: 1.0258
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5627 - loss: 1.0261
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5629 - loss: 1.0263
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5632 - loss: 1.0266
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5636 - loss: 1.0268
83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5639 - loss: 1.0268
86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5642 - loss: 1.0269
89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5645 - loss: 1.0270
92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5647 - loss: 1.0271
95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5648 - loss: 1.0273
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5649 - loss: 1.0274
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5651 - loss: 1.0275
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Epoch 5: val_accuracy did not improve from 0.56982
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5655 - loss: 1.0259 - val_accuracy: 0.4173 - val_loss: 1.4831 - learning_rate: 0.0010
Epoch 6/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:20 170ms/step - accuracy: 0.4062 - loss: 1.2991
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4688 - loss: 1.1953
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4862 - loss: 1.1581
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5006 - loss: 1.1296
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5092 - loss: 1.1114
15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5126 - loss: 1.1042
18/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5178 - loss: 1.0945
21/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5219 - loss: 1.0871
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5265 - loss: 1.0800
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5302 - loss: 1.0736
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5336 - loss: 1.0676
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5364 - loss: 1.0628
36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5388 - loss: 1.0585
39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5411 - loss: 1.0545
42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5432 - loss: 1.0509
44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5443 - loss: 1.0492
46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5455 - loss: 1.0477
49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5471 - loss: 1.0458
52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5485 - loss: 1.0440
55/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5497 - loss: 1.0423
58/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5508 - loss: 1.0409
61/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5518 - loss: 1.0396
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5529 - loss: 1.0384
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5539 - loss: 1.0372
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5549 - loss: 1.0360
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5558 - loss: 1.0346
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5567 - loss: 1.0333
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5575 - loss: 1.0321
82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5582 - loss: 1.0311
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5588 - loss: 1.0302
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5594 - loss: 1.0294
91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5600 - loss: 1.0286
94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5606 - loss: 1.0278
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5612 - loss: 1.0271
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5616 - loss: 1.0265
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5621 - loss: 1.0260
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5624 - loss: 1.0255
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5627 - loss: 1.0251
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5629 - loss: 1.0247
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5632 - loss: 1.0244
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5634 - loss: 1.0241
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5636 - loss: 1.0238
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5638 - loss: 1.0235
127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5640 - loss: 1.0232
130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5642 - loss: 1.0230
133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5643 - loss: 1.0227
136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5645 - loss: 1.0224
139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5647 - loss: 1.0222
142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5648 - loss: 1.0220
145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5649 - loss: 1.0217
148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5651 - loss: 1.0215
151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5652 - loss: 1.0213
154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5653 - loss: 1.0210
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5654 - loss: 1.0208
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5655 - loss: 1.0206
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5656 - loss: 1.0204
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5657 - loss: 1.0203
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5658 - loss: 1.0201
170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5659 - loss: 1.0199
173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5660 - loss: 1.0196
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5661 - loss: 1.0193
179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5663 - loss: 1.0190
182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5664 - loss: 1.0187
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5666 - loss: 1.0184
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5667 - loss: 1.0181
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5668 - loss: 1.0178
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5669 - loss: 1.0175
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5670 - loss: 1.0173
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5671 - loss: 1.0171
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5672 - loss: 1.0169
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5672 - loss: 1.0168
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5673 - loss: 1.0166
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5673 - loss: 1.0164
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5674 - loss: 1.0162
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5674 - loss: 1.0161
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5675 - loss: 1.0159
221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5675 - loss: 1.0158
224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5676 - loss: 1.0157
227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5677 - loss: 1.0155
230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5677 - loss: 1.0154
233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5677 - loss: 1.0153
236/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5677 - loss: 1.0151
239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5678 - loss: 1.0150
242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5678 - loss: 1.0149
245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5678 - loss: 1.0148
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5679 - loss: 1.0147
251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5679 - loss: 1.0146
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5679 - loss: 1.0145
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5679 - loss: 1.0145
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5679 - loss: 1.0144
262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0144
265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0143
268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0142
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0142
274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0141
277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0141
280/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0140
283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5680 - loss: 1.0140
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0139
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0139
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0138
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0138
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0138
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0137
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0137
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0136
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5682 - loss: 1.0136
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5682 - loss: 1.0135
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5682 - loss: 1.0135
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5682 - loss: 1.0134
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5682 - loss: 1.0134
324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5683 - loss: 1.0133
327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5683 - loss: 1.0133
329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5683 - loss: 1.0132
332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5683 - loss: 1.0132
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5684 - loss: 1.0131
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5684 - loss: 1.0130
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5684 - loss: 1.0130
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347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5685 - loss: 1.0129
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5685 - loss: 1.0128
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5685 - loss: 1.0128
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5685 - loss: 1.0127
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5686 - loss: 1.0127
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5686 - loss: 1.0127
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5686 - loss: 1.0126
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5686 - loss: 1.0126
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5686 - loss: 1.0125
374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5687 - loss: 1.0125
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5687 - loss: 1.0125
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5687 - loss: 1.0124
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5687 - loss: 1.0124
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5687 - loss: 1.0124
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5687 - loss: 1.0124
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0123
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0123
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0123
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0123
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0123
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0122
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0122
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0122
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0122
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0122
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0122
424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0123
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5688 - loss: 1.0123
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5688 - loss: 1.0123
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5688 - loss: 1.0123
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5688 - loss: 1.0123
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5688 - loss: 1.0123
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5688 - loss: 1.0123
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5688 - loss: 1.0123
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5689 - loss: 1.0123
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5689 - loss: 1.0123
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5689 - loss: 1.0123
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5689 - loss: 1.0123
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5689 - loss: 1.0123
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5689 - loss: 1.0123
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5689 - loss: 1.0123
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5689 - loss: 1.0123
Epoch 6: val_accuracy did not improve from 0.56982
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5689 - loss: 1.0123 - val_accuracy: 0.5268 - val_loss: 1.0536 - learning_rate: 0.0010
Epoch 7/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:19 168ms/step - accuracy: 0.5312 - loss: 1.0298
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5111 - loss: 1.0671
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5241 - loss: 1.0474
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5374 - loss: 1.0332
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5456 - loss: 1.0250
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5499 - loss: 1.0199
18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5519 - loss: 1.0170
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5560 - loss: 1.0133
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5587 - loss: 1.0106
26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5599 - loss: 1.0096
28/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5607 - loss: 1.0089
31/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5616 - loss: 1.0080
34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5622 - loss: 1.0068
37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5627 - loss: 1.0057
40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5632 - loss: 1.0046
42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5635 - loss: 1.0042
44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5638 - loss: 1.0039
47/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5641 - loss: 1.0038
50/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5645 - loss: 1.0035
53/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5645 - loss: 1.0036
56/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5646 - loss: 1.0037
59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5646 - loss: 1.0041
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5647 - loss: 1.0045
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5648 - loss: 1.0046
68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0045
71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5651 - loss: 1.0046
74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5652 - loss: 1.0046
77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5652 - loss: 1.0046
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5651 - loss: 1.0046
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5651 - loss: 1.0046
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0046
86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0047
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0048
91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5649 - loss: 1.0049
93/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5649 - loss: 1.0051
96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5649 - loss: 1.0052
99/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0053
102/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0054
105/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0054
108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5650 - loss: 1.0055
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5650 - loss: 1.0055
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5650 - loss: 1.0056
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5650 - loss: 1.0056
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5650 - loss: 1.0056
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5650 - loss: 1.0056
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5650 - loss: 1.0056
129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5651 - loss: 1.0055
132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5651 - loss: 1.0054
135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5652 - loss: 1.0053
137/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5652 - loss: 1.0053
140/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5653 - loss: 1.0052
143/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5654 - loss: 1.0051
145/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5655 - loss: 1.0050
148/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5656 - loss: 1.0049
151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5656 - loss: 1.0047
154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5657 - loss: 1.0046
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5658 - loss: 1.0046
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5658 - loss: 1.0045
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5659 - loss: 1.0044
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5661 - loss: 1.0043
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5662 - loss: 1.0042
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5663 - loss: 1.0041
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5664 - loss: 1.0040
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5664 - loss: 1.0040
179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5665 - loss: 1.0040
182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5665 - loss: 1.0040
185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5665 - loss: 1.0040
188/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5666 - loss: 1.0040
191/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5666 - loss: 1.0040
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5666 - loss: 1.0040
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5666 - loss: 1.0041
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5666 - loss: 1.0041
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5666 - loss: 1.0042
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5667 - loss: 1.0042
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5667 - loss: 1.0042
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5667 - loss: 1.0042
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5668 - loss: 1.0042
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5668 - loss: 1.0042
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5668 - loss: 1.0043
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5669 - loss: 1.0043
226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5669 - loss: 1.0043
229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5669 - loss: 1.0043
232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5670 - loss: 1.0043
235/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5670 - loss: 1.0042
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5671 - loss: 1.0042
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5671 - loss: 1.0042
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5671 - loss: 1.0042
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5672 - loss: 1.0042
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5672 - loss: 1.0042
251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5672 - loss: 1.0042
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5673 - loss: 1.0042
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5673 - loss: 1.0041
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5674 - loss: 1.0041
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5674 - loss: 1.0041
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5674 - loss: 1.0041
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5675 - loss: 1.0040
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5676 - loss: 1.0040
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5676 - loss: 1.0039
276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5677 - loss: 1.0039
279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5677 - loss: 1.0038
282/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5678 - loss: 1.0038
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5678 - loss: 1.0037
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302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5679 - loss: 1.0037
305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5680 - loss: 1.0037
308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5680 - loss: 1.0036
311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5680 - loss: 1.0036
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5680 - loss: 1.0036
317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5680 - loss: 1.0036
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5680 - loss: 1.0036
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5680 - loss: 1.0036
326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5680 - loss: 1.0036
329/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5680 - loss: 1.0037
332/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5680 - loss: 1.0037
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5680 - loss: 1.0037
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5680 - loss: 1.0037
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5680 - loss: 1.0037
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347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5680 - loss: 1.0037
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5680 - loss: 1.0037
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5680 - loss: 1.0037
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5680 - loss: 1.0037
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365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5680 - loss: 1.0037
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5680 - loss: 1.0037
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5679 - loss: 1.0037
374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5679 - loss: 1.0037
377/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5679 - loss: 1.0037
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5679 - loss: 1.0038
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5679 - loss: 1.0038
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5679 - loss: 1.0038
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5679 - loss: 1.0038
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5679 - loss: 1.0038
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5679 - loss: 1.0038
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5679 - loss: 1.0038
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5678 - loss: 1.0039
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5678 - loss: 1.0039
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5678 - loss: 1.0039
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5678 - loss: 1.0039
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5678 - loss: 1.0039
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5678 - loss: 1.0040
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5678 - loss: 1.0040
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5678 - loss: 1.0040
424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5678 - loss: 1.0040
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0040
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0040
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0040
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0041
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0041
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0041
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0041
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0041
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0041
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5679 - loss: 1.0041
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5679 - loss: 1.0042
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5679 - loss: 1.0042
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5679 - loss: 1.0042
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5679 - loss: 1.0042
Epoch 7: val_accuracy did not improve from 0.56982
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5679 - loss: 1.0042 - val_accuracy: 0.4266 - val_loss: 1.3867 - learning_rate: 0.0010
Epoch 8/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 167ms/step - accuracy: 0.6562 - loss: 0.9789
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6530 - loss: 0.9758
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6320 - loss: 0.9878
9/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6263 - loss: 0.9901
12/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6179 - loss: 0.9954
15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6087 - loss: 0.9991
18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6019 - loss: 1.0019
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5968 - loss: 1.0023
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5930 - loss: 1.0016
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5907 - loss: 1.0007
30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5892 - loss: 0.9992
33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5877 - loss: 0.9982
36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5865 - loss: 0.9979
39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5854 - loss: 0.9977
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5848 - loss: 0.9969
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5842 - loss: 0.9966
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5837 - loss: 0.9964
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5832 - loss: 0.9964
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5830 - loss: 0.9961
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5829 - loss: 0.9957
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5829 - loss: 0.9953
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5829 - loss: 0.9951
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5829 - loss: 0.9947
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5827 - loss: 0.9945
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5823 - loss: 0.9946
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5821 - loss: 0.9947
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5817 - loss: 0.9950
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5814 - loss: 0.9951
84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5811 - loss: 0.9953
87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5808 - loss: 0.9955
90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5806 - loss: 0.9956
92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5804 - loss: 0.9957
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5803 - loss: 0.9958
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5801 - loss: 0.9957
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5799 - loss: 0.9957
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5798 - loss: 0.9956
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5798 - loss: 0.9954
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5798 - loss: 0.9952
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5798 - loss: 0.9950
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5798 - loss: 0.9947
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5798 - loss: 0.9945
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5798 - loss: 0.9943
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5798 - loss: 0.9941
127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5798 - loss: 0.9939
130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5798 - loss: 0.9937
133/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5798 - loss: 0.9935
136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5798 - loss: 0.9933
139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5798 - loss: 0.9931
142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5798 - loss: 0.9930
145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5798 - loss: 0.9928
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5798 - loss: 0.9927
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5798 - loss: 0.9926
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5798 - loss: 0.9925
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5798 - loss: 0.9923
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5799 - loss: 0.9922
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5799 - loss: 0.9921
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5799 - loss: 0.9920
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5799 - loss: 0.9919
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5798 - loss: 0.9919
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5798 - loss: 0.9918
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5798 - loss: 0.9918
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5798 - loss: 0.9918
180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5798 - loss: 0.9917
183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5797 - loss: 0.9917
186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5797 - loss: 0.9917
189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5797 - loss: 0.9917
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5797 - loss: 0.9917
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5797 - loss: 0.9917
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5796 - loss: 0.9917
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5796 - loss: 0.9917
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5796 - loss: 0.9918
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5796 - loss: 0.9918
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5795 - loss: 0.9919
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5795 - loss: 0.9919
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5794 - loss: 0.9920
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5794 - loss: 0.9921
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5793 - loss: 0.9921
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5793 - loss: 0.9922
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5792 - loss: 0.9923
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5792 - loss: 0.9924
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5791 - loss: 0.9925
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5791 - loss: 0.9925
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5790 - loss: 0.9926
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5790 - loss: 0.9927
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5789 - loss: 0.9928
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5789 - loss: 0.9929
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5788 - loss: 0.9929
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5788 - loss: 0.9930
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5787 - loss: 0.9931
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5787 - loss: 0.9931
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5786 - loss: 0.9932
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5785 - loss: 0.9933
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5785 - loss: 0.9933
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5784 - loss: 0.9934
276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5784 - loss: 0.9935
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5783 - loss: 0.9936
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5783 - loss: 0.9936
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5782 - loss: 0.9937
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5782 - loss: 0.9938
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5781 - loss: 0.9939
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5781 - loss: 0.9939
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5780 - loss: 0.9940
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5780 - loss: 0.9941
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5780 - loss: 0.9942
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5779 - loss: 0.9943
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5779 - loss: 0.9943
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5779 - loss: 0.9944
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5778 - loss: 0.9945
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5778 - loss: 0.9946
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5778 - loss: 0.9946
324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5778 - loss: 0.9947
327/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5777 - loss: 0.9947
330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5777 - loss: 0.9948
333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5777 - loss: 0.9949
336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5776 - loss: 0.9949
339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5776 - loss: 0.9950
342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5776 - loss: 0.9950
345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5775 - loss: 0.9951
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5775 - loss: 0.9952
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5775 - loss: 0.9952
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5775 - loss: 0.9953
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5774 - loss: 0.9953
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5774 - loss: 0.9953
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5774 - loss: 0.9954
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5774 - loss: 0.9954
369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5774 - loss: 0.9954
372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5773 - loss: 0.9955
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5773 - loss: 0.9955
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5773 - loss: 0.9956
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5772 - loss: 0.9956
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5772 - loss: 0.9956
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5772 - loss: 0.9957
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5771 - loss: 0.9957
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5771 - loss: 0.9957
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5771 - loss: 0.9957
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5770 - loss: 0.9958
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5770 - loss: 0.9958
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5770 - loss: 0.9958
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5770 - loss: 0.9959
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5769 - loss: 0.9959
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5769 - loss: 0.9959
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5769 - loss: 0.9959
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5769 - loss: 0.9960
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5768 - loss: 0.9960
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5768 - loss: 0.9960
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5768 - loss: 0.9960
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5768 - loss: 0.9961
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5768 - loss: 0.9961
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5767 - loss: 0.9961
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5767 - loss: 0.9961
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5767 - loss: 0.9961
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5767 - loss: 0.9961
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5767 - loss: 0.9961
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5767 - loss: 0.9962
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5766 - loss: 0.9962
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5766 - loss: 0.9962
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5766 - loss: 0.9962
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5766 - loss: 0.9962
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5766 - loss: 0.9962
Epoch 8: val_accuracy did not improve from 0.56982
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5765 - loss: 0.9963 - val_accuracy: 0.4535 - val_loss: 1.2874 - learning_rate: 0.0010
Epoch 9/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 167ms/step - accuracy: 0.5938 - loss: 0.8266
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5866 - loss: 0.8793
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5740 - loss: 0.9336
9/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5718 - loss: 0.9489
12/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5729 - loss: 0.9609
15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5765 - loss: 0.9611
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368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5807 - loss: 0.9884
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374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5807 - loss: 0.9885
377/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5806 - loss: 0.9885
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5806 - loss: 0.9886
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5805 - loss: 0.9886
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5805 - loss: 0.9887
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5804 - loss: 0.9887
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5804 - loss: 0.9888
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5804 - loss: 0.9888
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5803 - loss: 0.9888
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5803 - loss: 0.9888
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5803 - loss: 0.9889
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5802 - loss: 0.9889
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5802 - loss: 0.9889
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5802 - loss: 0.9890
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5801 - loss: 0.9890
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5801 - loss: 0.9890
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5801 - loss: 0.9890
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5800 - loss: 0.9891
424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5800 - loss: 0.9891
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5800 - loss: 0.9891
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5800 - loss: 0.9891
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5800 - loss: 0.9891
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5799 - loss: 0.9892
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5799 - loss: 0.9892
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5799 - loss: 0.9892
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5798 - loss: 0.9892
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5798 - loss: 0.9893
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5797 - loss: 0.9893
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5797 - loss: 0.9893
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5797 - loss: 0.9894
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5796 - loss: 0.9894
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5796 - loss: 0.9894
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5796 - loss: 0.9894
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5795 - loss: 0.9895
Epoch 9: val_accuracy did not improve from 0.56982
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5795 - loss: 0.9895 - val_accuracy: 0.5329 - val_loss: 1.4058 - learning_rate: 0.0010
Epoch 10/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:20 171ms/step - accuracy: 0.6562 - loss: 0.8816
3/473 ━━━━━━━━━━━━━━━━━━━━ 14s 31ms/step - accuracy: 0.6372 - loss: 0.8518
5/473 ━━━━━━━━━━━━━━━━━━━━ 16s 35ms/step - accuracy: 0.6017 - loss: 0.8977
8/473 ━━━━━━━━━━━━━━━━━━━━ 13s 29ms/step - accuracy: 0.5844 - loss: 0.9205
11/473 ━━━━━━━━━━━━━━━━━━━━ 11s 26ms/step - accuracy: 0.5775 - loss: 0.9351
14/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5759 - loss: 0.9412
17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5759 - loss: 0.9437
20/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5769 - loss: 0.9452
23/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5784 - loss: 0.9455
26/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5789 - loss: 0.9474
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5786 - loss: 0.9499
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5780 - loss: 0.9527
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5768 - loss: 0.9556
38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5758 - loss: 0.9577
41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5753 - loss: 0.9598
44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5748 - loss: 0.9621
46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5747 - loss: 0.9635
48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5746 - loss: 0.9646
51/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5746 - loss: 0.9662
54/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5747 - loss: 0.9674
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5746 - loss: 0.9686
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5746 - loss: 0.9694
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5747 - loss: 0.9700
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5749 - loss: 0.9704
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5753 - loss: 0.9707
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5756 - loss: 0.9712
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5758 - loss: 0.9716
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5760 - loss: 0.9721
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5762 - loss: 0.9725
83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5763 - loss: 0.9728
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5764 - loss: 0.9732
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5766 - loss: 0.9738
91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5767 - loss: 0.9744
93/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5769 - loss: 0.9748
96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5770 - loss: 0.9754
99/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5772 - loss: 0.9760
102/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5773 - loss: 0.9766
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5775 - loss: 0.9769
106/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5776 - loss: 0.9771
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5779 - loss: 0.9774
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5781 - loss: 0.9777
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5784 - loss: 0.9780
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5786 - loss: 0.9783
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5788 - loss: 0.9786
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5790 - loss: 0.9788
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5792 - loss: 0.9790
129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5794 - loss: 0.9791
132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5797 - loss: 0.9793
134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5798 - loss: 0.9794
137/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5800 - loss: 0.9795
140/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5803 - loss: 0.9797
143/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5805 - loss: 0.9798
145/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5807 - loss: 0.9799
147/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5808 - loss: 0.9799
149/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5809 - loss: 0.9800
152/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5812 - loss: 0.9802
155/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5814 - loss: 0.9803
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5816 - loss: 0.9803
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5818 - loss: 0.9804
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5820 - loss: 0.9805
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5822 - loss: 0.9806
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5823 - loss: 0.9807
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5825 - loss: 0.9809
175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5826 - loss: 0.9810
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5827 - loss: 0.9811
179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5828 - loss: 0.9812
182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5830 - loss: 0.9813
185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5831 - loss: 0.9815
188/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5832 - loss: 0.9815
190/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5833 - loss: 0.9816
193/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5834 - loss: 0.9817
196/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5835 - loss: 0.9818
199/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5836 - loss: 0.9818
201/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5837 - loss: 0.9819
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5838 - loss: 0.9820
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5839 - loss: 0.9820
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5839 - loss: 0.9821
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5840 - loss: 0.9822
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5841 - loss: 0.9822
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5842 - loss: 0.9823
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5842 - loss: 0.9824
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5843 - loss: 0.9824
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5844 - loss: 0.9825
231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5844 - loss: 0.9825
234/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5845 - loss: 0.9825
237/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5846 - loss: 0.9826
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Epoch 10: val_accuracy improved from 0.56982 to 0.58409, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5870 - loss: 0.9811 - val_accuracy: 0.5841 - val_loss: 0.9809 - learning_rate: 0.0010
Epoch 11/40
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131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5781 - loss: 0.9896
134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5781 - loss: 0.9895
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5781 - loss: 0.9895
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143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5781 - loss: 0.9893
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152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5781 - loss: 0.9891
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159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5782 - loss: 0.9889
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188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5783 - loss: 0.9887
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196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5784 - loss: 0.9886
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213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5786 - loss: 0.9883
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234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5788 - loss: 0.9881
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5788 - loss: 0.9881
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243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5789 - loss: 0.9880
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251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5789 - loss: 0.9879
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5789 - loss: 0.9879
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5789 - loss: 0.9879
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271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5789 - loss: 0.9878
274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5789 - loss: 0.9878
277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5789 - loss: 0.9877
280/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5789 - loss: 0.9877
283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5789 - loss: 0.9877
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5789 - loss: 0.9877
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294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5789 - loss: 0.9877
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377/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5785 - loss: 0.9879
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5785 - loss: 0.9879
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395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5785 - loss: 0.9880
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407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5785 - loss: 0.9880
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418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5784 - loss: 0.9881
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427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5784 - loss: 0.9881
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5784 - loss: 0.9881
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435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5784 - loss: 0.9881
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881
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444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9880
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9880
Epoch 11: val_accuracy did not improve from 0.58409
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5783 - loss: 0.9880 - val_accuracy: 0.4774 - val_loss: 1.3026 - learning_rate: 0.0010
Epoch 12/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 165ms/step - accuracy: 0.5938 - loss: 0.8676
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5658 - loss: 0.9366
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344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5787 - loss: 0.9885
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5787 - loss: 0.9885
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5788 - loss: 0.9884
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5788 - loss: 0.9883
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5789 - loss: 0.9883
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5789 - loss: 0.9882
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5789 - loss: 0.9882
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5790 - loss: 0.9881
369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5790 - loss: 0.9881
372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5790 - loss: 0.9880
375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5790 - loss: 0.9880
378/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5791 - loss: 0.9880
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5791 - loss: 0.9879
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5791 - loss: 0.9879
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5792 - loss: 0.9879
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5792 - loss: 0.9878
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5792 - loss: 0.9878
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5793 - loss: 0.9878
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5793 - loss: 0.9877
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5793 - loss: 0.9877
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5794 - loss: 0.9876
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5794 - loss: 0.9876
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5794 - loss: 0.9875
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5795 - loss: 0.9875
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5795 - loss: 0.9874
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5795 - loss: 0.9874
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5796 - loss: 0.9873
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5796 - loss: 0.9873
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5796 - loss: 0.9873
425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5797 - loss: 0.9872
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5797 - loss: 0.9872
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5797 - loss: 0.9872
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5798 - loss: 0.9871
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5798 - loss: 0.9871
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5798 - loss: 0.9871
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5798 - loss: 0.9870
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5799 - loss: 0.9870
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5799 - loss: 0.9869
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5799 - loss: 0.9869
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5800 - loss: 0.9869
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5800 - loss: 0.9868
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5800 - loss: 0.9868
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5800 - loss: 0.9868
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5801 - loss: 0.9867
Epoch 12: val_accuracy did not improve from 0.58409
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5802 - loss: 0.9866 - val_accuracy: 0.5525 - val_loss: 1.1757 - learning_rate: 0.0010
Epoch 13/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:24 179ms/step - accuracy: 0.5938 - loss: 0.9203
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5677 - loss: 0.9651
6/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5554 - loss: 0.9847
8/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5524 - loss: 0.9887
11/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5526 - loss: 0.9916
14/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5528 - loss: 0.9950
17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5535 - loss: 0.9971
19/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5537 - loss: 0.9981
21/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5543 - loss: 0.9991
23/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5554 - loss: 0.9993
26/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5569 - loss: 1.0002
29/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5588 - loss: 0.9997
32/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5606 - loss: 0.9986
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5621 - loss: 0.9974
38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5633 - loss: 0.9965
41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5644 - loss: 0.9959
44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5656 - loss: 0.9956
47/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5666 - loss: 0.9954
49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5673 - loss: 0.9952
52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5682 - loss: 0.9949
55/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5692 - loss: 0.9944
57/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5698 - loss: 0.9942
59/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5703 - loss: 0.9939
62/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5712 - loss: 0.9931
64/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5717 - loss: 0.9927
66/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5720 - loss: 0.9923
68/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5723 - loss: 0.9920
70/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5726 - loss: 0.9916
73/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5729 - loss: 0.9911
76/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5732 - loss: 0.9906
78/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5735 - loss: 0.9903
81/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5737 - loss: 0.9899
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5741 - loss: 0.9895
86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5742 - loss: 0.9893
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5744 - loss: 0.9891
91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5746 - loss: 0.9889
94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5748 - loss: 0.9886
97/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5750 - loss: 0.9883
100/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5751 - loss: 0.9883
103/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5753 - loss: 0.9882
105/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5753 - loss: 0.9881
108/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5754 - loss: 0.9881
111/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5755 - loss: 0.9880
114/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5756 - loss: 0.9879
117/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5757 - loss: 0.9878
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 23ms/step - accuracy: 0.5758 - loss: 0.9877
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 23ms/step - accuracy: 0.5759 - loss: 0.9876
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5760 - loss: 0.9876
129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5760 - loss: 0.9876
132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5760 - loss: 0.9876
135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5761 - loss: 0.9876
138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5761 - loss: 0.9875
141/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5762 - loss: 0.9873
143/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5763 - loss: 0.9873
146/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5764 - loss: 0.9872
149/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5765 - loss: 0.9871
152/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5767 - loss: 0.9870
155/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5768 - loss: 0.9869
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5769 - loss: 0.9868
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5771 - loss: 0.9867
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5772 - loss: 0.9866
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5773 - loss: 0.9865
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5774 - loss: 0.9865
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5774 - loss: 0.9865
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5775 - loss: 0.9864
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5776 - loss: 0.9864
180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5776 - loss: 0.9864
183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5777 - loss: 0.9864
186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5777 - loss: 0.9864
189/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5778 - loss: 0.9864
192/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5778 - loss: 0.9864
195/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5779 - loss: 0.9863
198/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5780 - loss: 0.9863
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5780 - loss: 0.9863
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5781 - loss: 0.9863
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5781 - loss: 0.9862
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5782 - loss: 0.9862
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5782 - loss: 0.9861
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5783 - loss: 0.9861
218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5783 - loss: 0.9861
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5784 - loss: 0.9861
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5784 - loss: 0.9860
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5785 - loss: 0.9860
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5785 - loss: 0.9860
232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5786 - loss: 0.9860
235/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5786 - loss: 0.9859
238/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5786 - loss: 0.9859
241/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5787 - loss: 0.9858
243/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5787 - loss: 0.9858
245/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5787 - loss: 0.9857
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5788 - loss: 0.9857
251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5788 - loss: 0.9856
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5789 - loss: 0.9855
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5789 - loss: 0.9855
260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5790 - loss: 0.9854
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5790 - loss: 0.9853
266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5791 - loss: 0.9852
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5791 - loss: 0.9851
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5791 - loss: 0.9850
275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5792 - loss: 0.9850
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5792 - loss: 0.9849
281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5793 - loss: 0.9848
284/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5793 - loss: 0.9847
286/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5793 - loss: 0.9847
289/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5794 - loss: 0.9846
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5794 - loss: 0.9845
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5794 - loss: 0.9845
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5795 - loss: 0.9844
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5795 - loss: 0.9844
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5796 - loss: 0.9843
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5796 - loss: 0.9843
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5797 - loss: 0.9842
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5797 - loss: 0.9842
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5797 - loss: 0.9841
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5798 - loss: 0.9841
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5798 - loss: 0.9841
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5798 - loss: 0.9840
326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5799 - loss: 0.9839
329/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5799 - loss: 0.9839
332/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5800 - loss: 0.9838
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5800 - loss: 0.9837
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5801 - loss: 0.9837
339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5801 - loss: 0.9837
342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5801 - loss: 0.9836
345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5802 - loss: 0.9836
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5802 - loss: 0.9835
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5803 - loss: 0.9834
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5803 - loss: 0.9834
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5803 - loss: 0.9833
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5804 - loss: 0.9833
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5804 - loss: 0.9832
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5804 - loss: 0.9832
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5805 - loss: 0.9831
369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5805 - loss: 0.9831
372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5805 - loss: 0.9831
375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5806 - loss: 0.9830
378/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5806 - loss: 0.9830
380/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5806 - loss: 0.9829
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5806 - loss: 0.9829
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5807 - loss: 0.9828
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5807 - loss: 0.9828
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5807 - loss: 0.9827
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5808 - loss: 0.9827
397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5808 - loss: 0.9826
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5808 - loss: 0.9825
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5808 - loss: 0.9825
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5809 - loss: 0.9824
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5809 - loss: 0.9824
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5809 - loss: 0.9823
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5810 - loss: 0.9822
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5810 - loss: 0.9822
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5810 - loss: 0.9821
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5810 - loss: 0.9821
425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5811 - loss: 0.9820
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5811 - loss: 0.9819
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5811 - loss: 0.9819
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5812 - loss: 0.9818
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5812 - loss: 0.9818
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5812 - loss: 0.9817
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5813 - loss: 0.9816
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5813 - loss: 0.9816
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5813 - loss: 0.9815
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5814 - loss: 0.9815
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5814 - loss: 0.9814
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5814 - loss: 0.9814
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5814 - loss: 0.9814
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5814 - loss: 0.9813
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5815 - loss: 0.9813
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5815 - loss: 0.9813
Epoch 13: val_accuracy did not improve from 0.58409
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5815 - loss: 0.9812 - val_accuracy: 0.3514 - val_loss: 1.7125 - learning_rate: 0.0010
Epoch 14/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 167ms/step - accuracy: 0.5000 - loss: 1.4017
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5254 - loss: 1.2209
7/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5338 - loss: 1.1648
9/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.5320 - loss: 1.1506
12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5291 - loss: 1.1376
15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5318 - loss: 1.1239
18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5331 - loss: 1.1142
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5341 - loss: 1.1056
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5364 - loss: 1.0957
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5383 - loss: 1.0867
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5400 - loss: 1.0786
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5412 - loss: 1.0722
36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5423 - loss: 1.0667
39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5432 - loss: 1.0621
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5439 - loss: 1.0580
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5451 - loss: 1.0541
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5463 - loss: 1.0504
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5475 - loss: 1.0470
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5488 - loss: 1.0438
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5499 - loss: 1.0412
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5511 - loss: 1.0387
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5522 - loss: 1.0366
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5530 - loss: 1.0348
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5539 - loss: 1.0330
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5547 - loss: 1.0315
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5555 - loss: 1.0299
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5563 - loss: 1.0283
80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5568 - loss: 1.0273
83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5575 - loss: 1.0259
86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5583 - loss: 1.0245
89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5590 - loss: 1.0231
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438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5791 - loss: 0.9837
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444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5791 - loss: 0.9836
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5791 - loss: 0.9836
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5792 - loss: 0.9836
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5792 - loss: 0.9835
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5792 - loss: 0.9835
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5792 - loss: 0.9835
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5793 - loss: 0.9834
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5793 - loss: 0.9834
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5793 - loss: 0.9833
Epoch 14: val_accuracy did not improve from 0.58409
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5793 - loss: 0.9833 - val_accuracy: 0.5754 - val_loss: 0.9672 - learning_rate: 0.0010
Epoch 15/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:19 169ms/step - accuracy: 0.6250 - loss: 0.8782
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6445 - loss: 0.8743
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6353 - loss: 0.9044
9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6343 - loss: 0.9076
11/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.6337 - loss: 0.9085
14/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6295 - loss: 0.9135
17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6273 - loss: 0.9151
20/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6244 - loss: 0.9172
23/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6222 - loss: 0.9200
26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6197 - loss: 0.9239
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6177 - loss: 0.9273
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6161 - loss: 0.9298
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6144 - loss: 0.9316
38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6130 - loss: 0.9333
40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6125 - loss: 0.9341
43/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6117 - loss: 0.9351
46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6110 - loss: 0.9362
49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6104 - loss: 0.9370
52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6098 - loss: 0.9377
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6091 - loss: 0.9386
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6084 - loss: 0.9396
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6076 - loss: 0.9407
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6069 - loss: 0.9416
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6062 - loss: 0.9426
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6056 - loss: 0.9434
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6051 - loss: 0.9442
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6046 - loss: 0.9450
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6042 - loss: 0.9458
82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6038 - loss: 0.9464
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6034 - loss: 0.9470
87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6032 - loss: 0.9474
90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6029 - loss: 0.9479
92/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6026 - loss: 0.9482
95/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6022 - loss: 0.9487
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6019 - loss: 0.9492
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6015 - loss: 0.9495
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6012 - loss: 0.9499
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6010 - loss: 0.9501
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6007 - loss: 0.9505
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6004 - loss: 0.9508
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6002 - loss: 0.9510
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6001 - loss: 0.9511
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5998 - loss: 0.9513
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5997 - loss: 0.9514
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5994 - loss: 0.9516
128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5993 - loss: 0.9518
130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5991 - loss: 0.9521
133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5989 - loss: 0.9523
136/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5986 - loss: 0.9526
139/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5984 - loss: 0.9529
142/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5982 - loss: 0.9531
145/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5980 - loss: 0.9533
148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5977 - loss: 0.9535
151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5975 - loss: 0.9538
154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5973 - loss: 0.9541
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5970 - loss: 0.9543
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5969 - loss: 0.9545
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5967 - loss: 0.9547
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5966 - loss: 0.9550
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5964 - loss: 0.9551
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5963 - loss: 0.9554
175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5961 - loss: 0.9556
178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5960 - loss: 0.9558
181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5959 - loss: 0.9560
184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5958 - loss: 0.9562
187/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5956 - loss: 0.9565
190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5955 - loss: 0.9567
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5954 - loss: 0.9569
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5952 - loss: 0.9571
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5951 - loss: 0.9573
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5949 - loss: 0.9576
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5948 - loss: 0.9577
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5947 - loss: 0.9579
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5946 - loss: 0.9581
212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5945 - loss: 0.9582
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5945 - loss: 0.9583
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5944 - loss: 0.9585
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5943 - loss: 0.9586
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5943 - loss: 0.9586
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5943 - loss: 0.9587
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5942 - loss: 0.9588
231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5942 - loss: 0.9589
234/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5942 - loss: 0.9589
237/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5941 - loss: 0.9590
239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5941 - loss: 0.9591
242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5941 - loss: 0.9591
245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5941 - loss: 0.9592
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5941 - loss: 0.9592
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9593
253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9593
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9594
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9594
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9595
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9595
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9596
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5939 - loss: 0.9596
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5939 - loss: 0.9597
276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5939 - loss: 0.9598
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5938 - loss: 0.9599
281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5938 - loss: 0.9600
284/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5937 - loss: 0.9601
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5937 - loss: 0.9602
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5937 - loss: 0.9603
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5936 - loss: 0.9603
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5936 - loss: 0.9604
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5935 - loss: 0.9605
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5935 - loss: 0.9606
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5934 - loss: 0.9606
305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5934 - loss: 0.9607
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5934 - loss: 0.9608
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5933 - loss: 0.9608
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Epoch 15: val_accuracy improved from 0.58409 to 0.61141, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5923 - loss: 0.9639 - val_accuracy: 0.6114 - val_loss: 0.9456 - learning_rate: 0.0010
Epoch 16/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:19 168ms/step - accuracy: 0.5000 - loss: 1.1445
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201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5876 - loss: 0.9715
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5876 - loss: 0.9715
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5876 - loss: 0.9715
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5877 - loss: 0.9715
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5877 - loss: 0.9715
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5877 - loss: 0.9715
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5877 - loss: 0.9715
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5877 - loss: 0.9715
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5877 - loss: 0.9715
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9715
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9715
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9715
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9715
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9714
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9714
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9714
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9714
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9713
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9713
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9714
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9714
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9714
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9714
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9715
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9715
276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5876 - loss: 0.9715
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5876 - loss: 0.9715
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5876 - loss: 0.9715
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5876 - loss: 0.9715
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5876 - loss: 0.9715
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9714
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9714
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9714
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9714
324/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5875 - loss: 0.9713
326/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5875 - loss: 0.9713
329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5875 - loss: 0.9713
332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5875 - loss: 0.9713
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5876 - loss: 0.9713
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5876 - loss: 0.9713
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5876 - loss: 0.9712
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5876 - loss: 0.9712
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5876 - loss: 0.9712
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5876 - loss: 0.9711
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9711
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9711
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5877 - loss: 0.9710
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5877 - loss: 0.9710
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9710
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9708
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9708
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5879 - loss: 0.9708
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5879 - loss: 0.9708
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5879 - loss: 0.9708
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5879 - loss: 0.9707
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9708
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9708
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5878 - loss: 0.9708
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5878 - loss: 0.9708
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5878 - loss: 0.9709
Epoch 16: val_accuracy did not improve from 0.61141
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5877 - loss: 0.9710 - val_accuracy: 0.6108 - val_loss: 0.9286 - learning_rate: 0.0010
Epoch 17/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:19 169ms/step - accuracy: 0.7500 - loss: 0.8049
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6921 - loss: 0.8329
6/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6791 - loss: 0.8501
9/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6700 - loss: 0.8632
12/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6632 - loss: 0.8720
15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6576 - loss: 0.8791
17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6558 - loss: 0.8823
20/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6539 - loss: 0.8852
23/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6515 - loss: 0.8879
26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6487 - loss: 0.8905
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6457 - loss: 0.8937
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6433 - loss: 0.8962
34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6419 - loss: 0.8979
36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6406 - loss: 0.8999
39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6382 - loss: 0.9035
42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6355 - loss: 0.9074
45/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6333 - loss: 0.9106
48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6313 - loss: 0.9136
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6295 - loss: 0.9163
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6276 - loss: 0.9189
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6257 - loss: 0.9214
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6242 - loss: 0.9236
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6227 - loss: 0.9257
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6218 - loss: 0.9269
68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6206 - loss: 0.9287
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6199 - loss: 0.9297
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6190 - loss: 0.9309
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6182 - loss: 0.9319
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6176 - loss: 0.9327
82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6170 - loss: 0.9335
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438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5958 - loss: 0.9629
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5958 - loss: 0.9630
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5957 - loss: 0.9631
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5957 - loss: 0.9631
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5956 - loss: 0.9632
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5956 - loss: 0.9632
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5955 - loss: 0.9633
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5955 - loss: 0.9633
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5954 - loss: 0.9634
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5954 - loss: 0.9634
Epoch 17: val_accuracy did not improve from 0.61141
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5952 - loss: 0.9635 - val_accuracy: 0.5632 - val_loss: 0.9927 - learning_rate: 0.0010
Epoch 18/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:24 179ms/step - accuracy: 0.6562 - loss: 0.7410
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6328 - loss: 0.8021
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6234 - loss: 0.8275
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6181 - loss: 0.8490
12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6150 - loss: 0.8608
15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6121 - loss: 0.8710
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6078 - loss: 0.8840
22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6050 - loss: 0.8917
25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6018 - loss: 0.8999
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6002 - loss: 0.9049
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5992 - loss: 0.9087
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5979 - loss: 0.9147
34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5974 - loss: 0.9176
36/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5972 - loss: 0.9200
39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5966 - loss: 0.9231
42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5962 - loss: 0.9261
45/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5961 - loss: 0.9283
48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5958 - loss: 0.9301
51/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5955 - loss: 0.9318
53/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5952 - loss: 0.9328
56/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5949 - loss: 0.9343
59/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5945 - loss: 0.9360
62/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5941 - loss: 0.9375
64/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5938 - loss: 0.9386
66/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5935 - loss: 0.9396
69/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5929 - loss: 0.9411
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5925 - loss: 0.9424
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5922 - loss: 0.9435
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5921 - loss: 0.9443
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5921 - loss: 0.9450
83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5920 - loss: 0.9454
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5919 - loss: 0.9459
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5917 - loss: 0.9466
91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5916 - loss: 0.9474
94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5915 - loss: 0.9480
97/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5914 - loss: 0.9486
100/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5914 - loss: 0.9491
103/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5913 - loss: 0.9497
106/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5912 - loss: 0.9502
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5912 - loss: 0.9507
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5912 - loss: 0.9512
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5911 - loss: 0.9516
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5911 - loss: 0.9521
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5910 - loss: 0.9525
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5910 - loss: 0.9528
127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5910 - loss: 0.9531
130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5910 - loss: 0.9534
133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5911 - loss: 0.9537
135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5911 - loss: 0.9538
138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5911 - loss: 0.9541
141/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5912 - loss: 0.9543
144/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5913 - loss: 0.9544
147/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5914 - loss: 0.9544
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5915 - loss: 0.9545
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5916 - loss: 0.9546
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5917 - loss: 0.9547
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5918 - loss: 0.9548
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5919 - loss: 0.9550
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5919 - loss: 0.9551
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5920 - loss: 0.9553
170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5920 - loss: 0.9555
173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5921 - loss: 0.9557
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5921 - loss: 0.9558
179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5921 - loss: 0.9560
182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5921 - loss: 0.9561
184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5921 - loss: 0.9562
187/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5921 - loss: 0.9563
190/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5922 - loss: 0.9565
192/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5922 - loss: 0.9565
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5922 - loss: 0.9567
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5922 - loss: 0.9568
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5922 - loss: 0.9569
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5922 - loss: 0.9570
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5922 - loss: 0.9571
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5922 - loss: 0.9572
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5923 - loss: 0.9573
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5923 - loss: 0.9574
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5923 - loss: 0.9575
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5923 - loss: 0.9576
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5923 - loss: 0.9577
226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5923 - loss: 0.9579
229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5924 - loss: 0.9580
231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5924 - loss: 0.9580
233/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5924 - loss: 0.9581
236/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5924 - loss: 0.9582
239/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5925 - loss: 0.9583
242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5925 - loss: 0.9584
245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5925 - loss: 0.9585
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5926 - loss: 0.9585
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5926 - loss: 0.9586
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5926 - loss: 0.9587
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5926 - loss: 0.9587
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9588
260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9589
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9590
266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9591
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9592
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9593
275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9595
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9596
281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5926 - loss: 0.9597
283/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5926 - loss: 0.9597
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5926 - loss: 0.9598
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5926 - loss: 0.9599
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5926 - loss: 0.9600
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5926 - loss: 0.9601
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5926 - loss: 0.9602
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9602
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9603
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9604
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9604
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9605
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9605
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9606
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9606
325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9606
328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9607
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5925 - loss: 0.9607
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5925 - loss: 0.9608
336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5925 - loss: 0.9608
339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5925 - loss: 0.9609
342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5925 - loss: 0.9609
345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5924 - loss: 0.9610
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5924 - loss: 0.9611
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5924 - loss: 0.9611
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5924 - loss: 0.9612
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5924 - loss: 0.9612
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5923 - loss: 0.9613
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5923 - loss: 0.9613
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5923 - loss: 0.9614
369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5923 - loss: 0.9615
372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5923 - loss: 0.9615
375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5923 - loss: 0.9616
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5923 - loss: 0.9616
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9617
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9618
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9618
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9618
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9619
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9619
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9619
397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9620
400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9621
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9621
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9622
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9622
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9622
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9623
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5920 - loss: 0.9623
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5920 - loss: 0.9624
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5920 - loss: 0.9624
424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5920 - loss: 0.9624
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5920 - loss: 0.9625
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5920 - loss: 0.9625
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5920 - loss: 0.9626
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9626
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9626
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9627
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9627
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9627
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9627
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9628
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9628
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9628
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9628
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9628
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5918 - loss: 0.9629
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5918 - loss: 0.9629
Epoch 18: val_accuracy did not improve from 0.61141
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5918 - loss: 0.9629 - val_accuracy: 0.5572 - val_loss: 1.0739 - learning_rate: 0.0010
Epoch 19/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:19 169ms/step - accuracy: 0.6250 - loss: 0.8319
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6178 - loss: 0.9040
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6025 - loss: 0.9162
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5960 - loss: 0.9319
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5919 - loss: 0.9398
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5905 - loss: 0.9414
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5907 - loss: 0.9416
22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5912 - loss: 0.9411
25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5917 - loss: 0.9403
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5922 - loss: 0.9396
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5927 - loss: 0.9393
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5931 - loss: 0.9401
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5929 - loss: 0.9414
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5926 - loss: 0.9430
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5924 - loss: 0.9445
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5921 - loss: 0.9457
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5918 - loss: 0.9467
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5916 - loss: 0.9475
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5912 - loss: 0.9485
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5907 - loss: 0.9495
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5902 - loss: 0.9504
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5897 - loss: 0.9512
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5894 - loss: 0.9518
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5891 - loss: 0.9523
73/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5889 - loss: 0.9528
76/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5886 - loss: 0.9533
79/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5882 - loss: 0.9539
82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5878 - loss: 0.9545
85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5876 - loss: 0.9550
88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5874 - loss: 0.9553
91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5872 - loss: 0.9556
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5871 - loss: 0.9560
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5870 - loss: 0.9562
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5870 - loss: 0.9565
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5870 - loss: 0.9567
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5870 - loss: 0.9568
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5871 - loss: 0.9570
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5871 - loss: 0.9572
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5872 - loss: 0.9573
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5873 - loss: 0.9575
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5874 - loss: 0.9577
124/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5875 - loss: 0.9578
127/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5877 - loss: 0.9579
129/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5877 - loss: 0.9579
132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5879 - loss: 0.9580
135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5880 - loss: 0.9581
138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5881 - loss: 0.9582
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5881 - loss: 0.9583
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9584
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9585
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9586
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9587
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9588
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9589
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9589
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9591
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9591
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9593
174/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5882 - loss: 0.9594
177/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5882 - loss: 0.9595
180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5881 - loss: 0.9597
183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5881 - loss: 0.9599
186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5881 - loss: 0.9600
189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5880 - loss: 0.9602
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5880 - loss: 0.9603
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5880 - loss: 0.9604
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5880 - loss: 0.9605
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9606
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9607
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9608
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9608
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9609
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9610
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9610
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9611
225/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9612
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9612
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9613
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9613
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9614
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9614
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9614
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5881 - loss: 0.9615
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5881 - loss: 0.9615
273/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5881 - loss: 0.9615
276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5881 - loss: 0.9615
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5881 - loss: 0.9615
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5882 - loss: 0.9615
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5882 - loss: 0.9615
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5882 - loss: 0.9615
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5882 - loss: 0.9615
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5882 - loss: 0.9615
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5883 - loss: 0.9615
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5883 - loss: 0.9615
302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5883 - loss: 0.9616
305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5883 - loss: 0.9616
308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5883 - loss: 0.9616
311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5883 - loss: 0.9617
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5884 - loss: 0.9617
317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5884 - loss: 0.9617
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5884 - loss: 0.9617
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5884 - loss: 0.9618
325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9618
328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9618
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9618
333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9618
336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619
339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9620
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9620
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9620
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9621
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9621
369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9621
372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9622
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5885 - loss: 0.9622
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5885 - loss: 0.9622
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5885 - loss: 0.9622
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9623
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9623
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9623
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9624
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9624
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9624
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9624
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9625
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9625
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9625
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9626
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9626
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9626
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9627
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9627
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9627
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9627
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9628
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9628
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9628
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9629
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9629
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9629
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9630
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5887 - loss: 0.9630
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5887 - loss: 0.9630
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5887 - loss: 0.9631
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5887 - loss: 0.9631
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5887 - loss: 0.9631
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5887 - loss: 0.9631
Epoch 19: val_accuracy did not improve from 0.61141
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5887 - loss: 0.9632 - val_accuracy: 0.5433 - val_loss: 1.1012 - learning_rate: 0.0010
Epoch 20/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 164ms/step - accuracy: 0.7500 - loss: 0.7466
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.7031 - loss: 0.8127
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6855 - loss: 0.8420
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6754 - loss: 0.8626
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6679 - loss: 0.8791
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6633 - loss: 0.8895
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6587 - loss: 0.8995
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6531 - loss: 0.9093
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6480 - loss: 0.9158
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6450 - loss: 0.9192
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6420 - loss: 0.9227
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6384 - loss: 0.9267
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6356 - loss: 0.9294
38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6326 - loss: 0.9324
41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6295 - loss: 0.9358
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6265 - loss: 0.9390
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6242 - loss: 0.9411
49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6229 - loss: 0.9425
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6209 - loss: 0.9443
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6192 - loss: 0.9459
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6183 - loss: 0.9468
59/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6174 - loss: 0.9476
61/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6165 - loss: 0.9484
64/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6152 - loss: 0.9492
67/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6142 - loss: 0.9499
69/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6136 - loss: 0.9502
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6127 - loss: 0.9508
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6118 - loss: 0.9514
77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6114 - loss: 0.9517
80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6107 - loss: 0.9521
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443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9634
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9633
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9633
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9633
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9633
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9633
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9633
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5970 - loss: 0.9633
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5970 - loss: 0.9632
Epoch 20: val_accuracy did not improve from 0.61141
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5970 - loss: 0.9632 - val_accuracy: 0.5045 - val_loss: 1.1262 - learning_rate: 0.0010
Epoch 21/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 166ms/step - accuracy: 0.5625 - loss: 0.8969
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5788 - loss: 0.9398
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5934 - loss: 0.9419
9/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.6007 - loss: 0.9401
12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6114 - loss: 0.9353
15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6181 - loss: 0.9283
18/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6240 - loss: 0.9207
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6273 - loss: 0.9156
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6286 - loss: 0.9149
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6281 - loss: 0.9164
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6278 - loss: 0.9179
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6269 - loss: 0.9200
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6262 - loss: 0.9216
38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6250 - loss: 0.9242
41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6240 - loss: 0.9260
44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6231 - loss: 0.9278
46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6225 - loss: 0.9290
48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6219 - loss: 0.9302
51/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6209 - loss: 0.9317
54/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6200 - loss: 0.9331
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6190 - loss: 0.9345
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6181 - loss: 0.9359
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6173 - loss: 0.9374
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6165 - loss: 0.9386
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6160 - loss: 0.9397
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6155 - loss: 0.9406
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6152 - loss: 0.9412
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6149 - loss: 0.9416
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6145 - loss: 0.9421
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6141 - loss: 0.9428
87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6136 - loss: 0.9434
90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6132 - loss: 0.9439
93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6129 - loss: 0.9443
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6126 - loss: 0.9448
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6123 - loss: 0.9452
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6120 - loss: 0.9456
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6118 - loss: 0.9458
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6115 - loss: 0.9462
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6113 - loss: 0.9466
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6110 - loss: 0.9469
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6109 - loss: 0.9471
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6107 - loss: 0.9473
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6105 - loss: 0.9475
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6104 - loss: 0.9477
127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6102 - loss: 0.9479
129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6101 - loss: 0.9480
132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6100 - loss: 0.9482
135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6098 - loss: 0.9484
138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6096 - loss: 0.9486
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6095 - loss: 0.9488
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6093 - loss: 0.9491
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6091 - loss: 0.9494
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6089 - loss: 0.9497
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6088 - loss: 0.9500
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6085 - loss: 0.9503
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6083 - loss: 0.9507
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6081 - loss: 0.9510
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6078 - loss: 0.9514
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6076 - loss: 0.9517
170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6075 - loss: 0.9519
173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6073 - loss: 0.9522
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6071 - loss: 0.9526
179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6068 - loss: 0.9529
182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6066 - loss: 0.9533
185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6064 - loss: 0.9536
187/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6062 - loss: 0.9538
190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6059 - loss: 0.9542
193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6057 - loss: 0.9545
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6054 - loss: 0.9549
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6052 - loss: 0.9552
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6050 - loss: 0.9555
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6047 - loss: 0.9558
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6045 - loss: 0.9561
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6043 - loss: 0.9563
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6041 - loss: 0.9566
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6039 - loss: 0.9568
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6037 - loss: 0.9571
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6036 - loss: 0.9574
226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6034 - loss: 0.9576
229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6032 - loss: 0.9578
232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6030 - loss: 0.9581
235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6029 - loss: 0.9583
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6027 - loss: 0.9585
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6025 - loss: 0.9587
244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6024 - loss: 0.9589
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6022 - loss: 0.9591
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6021 - loss: 0.9593
253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6020 - loss: 0.9594
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6019 - loss: 0.9596
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6017 - loss: 0.9598
262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6016 - loss: 0.9599
265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6015 - loss: 0.9601
268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6014 - loss: 0.9602
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6012 - loss: 0.9604
274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6011 - loss: 0.9605
277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6010 - loss: 0.9607
280/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6009 - loss: 0.9608
283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6009 - loss: 0.9609
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6008 - loss: 0.9610
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6007 - loss: 0.9611
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6007 - loss: 0.9611
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6006 - loss: 0.9612
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6005 - loss: 0.9613
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6005 - loss: 0.9613
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6004 - loss: 0.9614
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6004 - loss: 0.9615
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6003 - loss: 0.9616
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6002 - loss: 0.9616
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6002 - loss: 0.9617
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6001 - loss: 0.9618
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6000 - loss: 0.9619
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6000 - loss: 0.9619
325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5999 - loss: 0.9620
328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5999 - loss: 0.9620
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5998 - loss: 0.9621
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5997 - loss: 0.9621
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5997 - loss: 0.9622
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5996 - loss: 0.9622
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5995 - loss: 0.9623
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5995 - loss: 0.9623
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5994 - loss: 0.9623
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5994 - loss: 0.9623
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5993 - loss: 0.9623
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5993 - loss: 0.9624
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5992 - loss: 0.9624
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5992 - loss: 0.9624
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5991 - loss: 0.9624
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5991 - loss: 0.9624
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5990 - loss: 0.9624
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5990 - loss: 0.9625
376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5989 - loss: 0.9625
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5989 - loss: 0.9625
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5988 - loss: 0.9625
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5988 - loss: 0.9626
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5987 - loss: 0.9626
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5987 - loss: 0.9626
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5986 - loss: 0.9626
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5986 - loss: 0.9627
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5986 - loss: 0.9627
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5985 - loss: 0.9627
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5985 - loss: 0.9627
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5984 - loss: 0.9628
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5984 - loss: 0.9628
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5983 - loss: 0.9628
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5982 - loss: 0.9629
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5982 - loss: 0.9629
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5981 - loss: 0.9629
425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5981 - loss: 0.9630
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5980 - loss: 0.9630
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5980 - loss: 0.9630
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5979 - loss: 0.9630
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5979 - loss: 0.9630
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5979 - loss: 0.9630
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5978 - loss: 0.9631
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5978 - loss: 0.9631
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5977 - loss: 0.9631
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5977 - loss: 0.9631
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5976 - loss: 0.9631
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5976 - loss: 0.9631
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5976 - loss: 0.9631
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5975 - loss: 0.9631
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5975 - loss: 0.9631
Epoch 21: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.
Epoch 21: val_accuracy did not improve from 0.61141
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5974 - loss: 0.9632 - val_accuracy: 0.5821 - val_loss: 0.9879 - learning_rate: 0.0010
Epoch 22/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:22 176ms/step - accuracy: 0.5312 - loss: 1.0048
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5814 - loss: 0.9163
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5817 - loss: 0.9304
9/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.5778 - loss: 0.9447
12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5812 - loss: 0.9461
15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5837 - loss: 0.9450
18/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5863 - loss: 0.9423
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5891 - loss: 0.9395
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5916 - loss: 0.9370
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5940 - loss: 0.9343
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5964 - loss: 0.9314
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5977 - loss: 0.9298
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5991 - loss: 0.9281
37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5999 - loss: 0.9271
40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6012 - loss: 0.9255
43/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6019 - loss: 0.9245
46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6026 - loss: 0.9236
49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6031 - loss: 0.9227
52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6036 - loss: 0.9220
54/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6039 - loss: 0.9215
56/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6042 - loss: 0.9211
58/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6045 - loss: 0.9208
60/473 ━━━━━━━━━━━━━━━━━━━━ 9s 24ms/step - accuracy: 0.6048 - loss: 0.9204
63/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6052 - loss: 0.9201
66/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6055 - loss: 0.9198
69/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6057 - loss: 0.9198
72/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6058 - loss: 0.9199
75/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6059 - loss: 0.9201
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.6059 - loss: 0.9204
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.6058 - loss: 0.9207
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6059 - loss: 0.9209
86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.6059 - loss: 0.9211
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.6058 - loss: 0.9213
91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.6059 - loss: 0.9214
94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.6059 - loss: 0.9215
97/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6060 - loss: 0.9216
100/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6060 - loss: 0.9217
103/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6061 - loss: 0.9217
105/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6061 - loss: 0.9218
108/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6061 - loss: 0.9219
111/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6061 - loss: 0.9220
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6061 - loss: 0.9221
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6061 - loss: 0.9222
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6061 - loss: 0.9222
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6062 - loss: 0.9222
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6062 - loss: 0.9222
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6062 - loss: 0.9221
128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 23ms/step - accuracy: 0.6063 - loss: 0.9221
131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 23ms/step - accuracy: 0.6064 - loss: 0.9220
134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6065 - loss: 0.9219
136/473 ━━━━━━━━━━━━━━━━━━━━ 7s 23ms/step - accuracy: 0.6066 - loss: 0.9219
139/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6067 - loss: 0.9218
142/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6068 - loss: 0.9218
144/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6069 - loss: 0.9217
147/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6070 - loss: 0.9217
149/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6071 - loss: 0.9217
152/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6071 - loss: 0.9217
155/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6072 - loss: 0.9217
157/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6072 - loss: 0.9217
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6073 - loss: 0.9217
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6073 - loss: 0.9217
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6074 - loss: 0.9217
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6074 - loss: 0.9217
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6074 - loss: 0.9217
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6074 - loss: 0.9217
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6075 - loss: 0.9217
180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6075 - loss: 0.9217
183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6075 - loss: 0.9217
186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6075 - loss: 0.9218
189/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6075 - loss: 0.9218
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Epoch 22: val_accuracy improved from 0.61141 to 0.63593, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6079 - loss: 0.9255 - val_accuracy: 0.6359 - val_loss: 0.8763 - learning_rate: 2.0000e-04
Epoch 23/40
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423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6158 - loss: 0.9224
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6157 - loss: 0.9225
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6157 - loss: 0.9225
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6157 - loss: 0.9225
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6157 - loss: 0.9225
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6157 - loss: 0.9226
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6157 - loss: 0.9226
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6156 - loss: 0.9226
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6156 - loss: 0.9227
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6156 - loss: 0.9227
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6156 - loss: 0.9227
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6156 - loss: 0.9227
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6156 - loss: 0.9227
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6155 - loss: 0.9228
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6155 - loss: 0.9228
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6155 - loss: 0.9228
471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6154 - loss: 0.9229
Epoch 23: val_accuracy did not improve from 0.63593
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6154 - loss: 0.9229 - val_accuracy: 0.6062 - val_loss: 0.9353 - learning_rate: 2.0000e-04
Epoch 24/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:19 168ms/step - accuracy: 0.5938 - loss: 0.8949
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6230 - loss: 0.8562
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6289 - loss: 0.8521
9/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6285 - loss: 0.8580
12/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6281 - loss: 0.8647
15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6305 - loss: 0.8660
18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6309 - loss: 0.8683
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6302 - loss: 0.8720
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6288 - loss: 0.8756
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6276 - loss: 0.8789
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6263 - loss: 0.8823
33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6246 - loss: 0.8862
36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6230 - loss: 0.8898
39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6212 - loss: 0.8939
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6205 - loss: 0.8965
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6200 - loss: 0.8984
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6195 - loss: 0.8995
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6191 - loss: 0.9006
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6185 - loss: 0.9024
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6179 - loss: 0.9039
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6174 - loss: 0.9053
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6170 - loss: 0.9063
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6165 - loss: 0.9075
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6159 - loss: 0.9087
68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6156 - loss: 0.9094
71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6151 - loss: 0.9104
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6149 - loss: 0.9111
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6146 - loss: 0.9116
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6142 - loss: 0.9124
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6139 - loss: 0.9130
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6135 - loss: 0.9137
87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6131 - loss: 0.9144
89/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6129 - loss: 0.9148
91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6126 - loss: 0.9153
94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6123 - loss: 0.9161
96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6121 - loss: 0.9166
99/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6118 - loss: 0.9174
102/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6114 - loss: 0.9184
104/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6112 - loss: 0.9189
107/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6108 - loss: 0.9198
110/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6105 - loss: 0.9207
112/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6103 - loss: 0.9212
114/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6101 - loss: 0.9216
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6099 - loss: 0.9223
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6096 - loss: 0.9228
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6095 - loss: 0.9233
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6093 - loss: 0.9238
129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6091 - loss: 0.9243
132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6089 - loss: 0.9247
135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6087 - loss: 0.9252
138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6086 - loss: 0.9255
141/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6085 - loss: 0.9259
144/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6084 - loss: 0.9262
147/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6084 - loss: 0.9264
150/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6083 - loss: 0.9267
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6082 - loss: 0.9269
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6082 - loss: 0.9270
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6082 - loss: 0.9272
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6081 - loss: 0.9274
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6081 - loss: 0.9276
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6080 - loss: 0.9278
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6080 - loss: 0.9280
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6079 - loss: 0.9281
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6079 - loss: 0.9282
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6079 - loss: 0.9284
179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6079 - loss: 0.9285
182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6078 - loss: 0.9287
185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6078 - loss: 0.9289
188/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6078 - loss: 0.9291
190/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6078 - loss: 0.9292
193/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6077 - loss: 0.9294
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9295
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9297
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9298
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9298
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9299
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9300
212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9301
215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9301
218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9301
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6078 - loss: 0.9302
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6078 - loss: 0.9302
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6078 - loss: 0.9302
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6079 - loss: 0.9302
231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6079 - loss: 0.9302
234/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6080 - loss: 0.9302
237/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6080 - loss: 0.9302
240/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6080 - loss: 0.9302
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6080 - loss: 0.9302
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6081 - loss: 0.9302
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6081 - loss: 0.9302
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6081 - loss: 0.9302
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6081 - loss: 0.9302
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6081 - loss: 0.9302
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6082 - loss: 0.9302
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6082 - loss: 0.9302
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6082 - loss: 0.9303
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6082 - loss: 0.9303
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6082 - loss: 0.9303
276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6082 - loss: 0.9303
279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6082 - loss: 0.9303
282/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6082 - loss: 0.9303
285/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6082 - loss: 0.9303
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6082 - loss: 0.9304
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6082 - loss: 0.9304
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Epoch 24: val_accuracy improved from 0.63593 to 0.63653, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.6093 - loss: 0.9295 - val_accuracy: 0.6365 - val_loss: 0.8852 - learning_rate: 2.0000e-04
Epoch 25/40
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194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6186 - loss: 0.9175
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6185 - loss: 0.9176
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6184 - loss: 0.9178
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6183 - loss: 0.9179
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6182 - loss: 0.9180
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6182 - loss: 0.9181
212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6181 - loss: 0.9182
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6181 - loss: 0.9183
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6180 - loss: 0.9184
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225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6179 - loss: 0.9185
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6178 - loss: 0.9185
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6178 - loss: 0.9185
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6177 - loss: 0.9186
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6177 - loss: 0.9186
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6176 - loss: 0.9186
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6176 - loss: 0.9187
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6176 - loss: 0.9187
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6175 - loss: 0.9188
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6175 - loss: 0.9188
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6174 - loss: 0.9189
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6174 - loss: 0.9189
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6174 - loss: 0.9190
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6173 - loss: 0.9190
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6173 - loss: 0.9190
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6173 - loss: 0.9191
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6173 - loss: 0.9191
276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.6173 - loss: 0.9191
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.6173 - loss: 0.9191
281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.6173 - loss: 0.9191
283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.6173 - loss: 0.9192
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.6172 - loss: 0.9192
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.6172 - loss: 0.9192
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6172 - loss: 0.9192
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6172 - loss: 0.9192
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6172 - loss: 0.9193
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6172 - loss: 0.9193
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6172 - loss: 0.9194
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6172 - loss: 0.9194
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9195
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9195
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9196
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9196
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9196
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9196
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9196
324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9196
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330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6171 - loss: 0.9196
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341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6171 - loss: 0.9197
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6171 - loss: 0.9197
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6171 - loss: 0.9197
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6171 - loss: 0.9197
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197
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371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197
376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9196
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9196
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9196
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9196
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9196
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9196
397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195
400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195
425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6171 - loss: 0.9195
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6170 - loss: 0.9196
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6170 - loss: 0.9196
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6170 - loss: 0.9196
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6170 - loss: 0.9197
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6170 - loss: 0.9197
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6170 - loss: 0.9197
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6169 - loss: 0.9197
Epoch 25: val_accuracy did not improve from 0.63653
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.6169 - loss: 0.9197 - val_accuracy: 0.6359 - val_loss: 0.8749 - learning_rate: 2.0000e-04
Epoch 26/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:20 170ms/step - accuracy: 0.6562 - loss: 0.7736
3/473 ━━━━━━━━━━━━━━━━━━━━ 12s 27ms/step - accuracy: 0.6858 - loss: 0.7739
5/473 ━━━━━━━━━━━━━━━━━━━━ 13s 30ms/step - accuracy: 0.6808 - loss: 0.8023
8/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.6619 - loss: 0.8551
11/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.6501 - loss: 0.8793
14/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6442 - loss: 0.8926
17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6401 - loss: 0.9011
20/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6367 - loss: 0.9068
23/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6331 - loss: 0.9122
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6292 - loss: 0.9172
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6270 - loss: 0.9207
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6258 - loss: 0.9226
36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6245 - loss: 0.9241
39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6235 - loss: 0.9248
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6225 - loss: 0.9253
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6215 - loss: 0.9258
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6203 - loss: 0.9264
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6194 - loss: 0.9267
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6185 - loss: 0.9269
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6178 - loss: 0.9269
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6172 - loss: 0.9267
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6168 - loss: 0.9264
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420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9179
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6145 - loss: 0.9179
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9179
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9181
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9181
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6146 - loss: 0.9181
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6146 - loss: 0.9182
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6146 - loss: 0.9182
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6146 - loss: 0.9182
Epoch 26: val_accuracy did not improve from 0.63653
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.6146 - loss: 0.9183 - val_accuracy: 0.6241 - val_loss: 0.9116 - learning_rate: 2.0000e-04
Epoch 27/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:22 174ms/step - accuracy: 0.6250 - loss: 0.9067
3/473 ━━━━━━━━━━━━━━━━━━━━ 14s 32ms/step - accuracy: 0.6111 - loss: 0.8998
5/473 ━━━━━━━━━━━━━━━━━━━━ 13s 28ms/step - accuracy: 0.5985 - loss: 0.9138
8/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5945 - loss: 0.9277
11/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5870 - loss: 0.9407
14/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5861 - loss: 0.9457
17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5876 - loss: 0.9462
20/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5902 - loss: 0.9433
23/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5938 - loss: 0.9391
26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5957 - loss: 0.9368
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5964 - loss: 0.9365
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5961 - loss: 0.9379
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5959 - loss: 0.9387
38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5963 - loss: 0.9385
41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5964 - loss: 0.9387
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5965 - loss: 0.9390
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5961 - loss: 0.9401
50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5958 - loss: 0.9410
53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5953 - loss: 0.9419
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5951 - loss: 0.9424
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5949 - loss: 0.9427
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5948 - loss: 0.9430
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5947 - loss: 0.9431
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5947 - loss: 0.9430
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5947 - loss: 0.9430
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5946 - loss: 0.9432
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5947 - loss: 0.9432
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5947 - loss: 0.9432
82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5948 - loss: 0.9432
85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5950 - loss: 0.9431
88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5951 - loss: 0.9431
91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5953 - loss: 0.9429
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5955 - loss: 0.9428
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5956 - loss: 0.9427
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5959 - loss: 0.9424
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5960 - loss: 0.9422
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5962 - loss: 0.9419
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5964 - loss: 0.9417
108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5966 - loss: 0.9415
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5968 - loss: 0.9413
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5970 - loss: 0.9411
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5971 - loss: 0.9410
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5973 - loss: 0.9409
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5975 - loss: 0.9408
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5976 - loss: 0.9407
129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5978 - loss: 0.9405
132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5980 - loss: 0.9404
135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5982 - loss: 0.9402
138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5985 - loss: 0.9400
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5987 - loss: 0.9398
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5988 - loss: 0.9396
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5990 - loss: 0.9394
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5991 - loss: 0.9393
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5993 - loss: 0.9392
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5994 - loss: 0.9390
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5995 - loss: 0.9389
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5996 - loss: 0.9387
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5997 - loss: 0.9385
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5999 - loss: 0.9384
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6000 - loss: 0.9382
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6001 - loss: 0.9380
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6003 - loss: 0.9378
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6004 - loss: 0.9377
179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6006 - loss: 0.9374
182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6008 - loss: 0.9372
185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6010 - loss: 0.9370
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6011 - loss: 0.9368
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6013 - loss: 0.9366
193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6014 - loss: 0.9364
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6015 - loss: 0.9363
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6017 - loss: 0.9361
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6018 - loss: 0.9360
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6019 - loss: 0.9359
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6020 - loss: 0.9357
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6021 - loss: 0.9355
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6022 - loss: 0.9354
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6023 - loss: 0.9352
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6024 - loss: 0.9350
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6025 - loss: 0.9348
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6026 - loss: 0.9346
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6028 - loss: 0.9344
231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6029 - loss: 0.9343
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6030 - loss: 0.9341
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6031 - loss: 0.9339
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6032 - loss: 0.9337
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6033 - loss: 0.9335
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6035 - loss: 0.9333
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6036 - loss: 0.9332
251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6037 - loss: 0.9329
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6038 - loss: 0.9327
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6039 - loss: 0.9325
260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6041 - loss: 0.9323
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6042 - loss: 0.9321
266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6043 - loss: 0.9320
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6044 - loss: 0.9318
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6044 - loss: 0.9317
275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6045 - loss: 0.9315
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6046 - loss: 0.9314
281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6047 - loss: 0.9312
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6048 - loss: 0.9311
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6049 - loss: 0.9309
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6050 - loss: 0.9308
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6050 - loss: 0.9306
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6051 - loss: 0.9305
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6052 - loss: 0.9304
302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6053 - loss: 0.9302
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Epoch 27: val_accuracy improved from 0.63653 to 0.65240, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.6087 - loss: 0.9251 - val_accuracy: 0.6524 - val_loss: 0.8566 - learning_rate: 2.0000e-04
Epoch 28/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:28 186ms/step - accuracy: 0.6250 - loss: 0.9379
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6276 - loss: 0.8992
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178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6248 - loss: 0.8999
181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6247 - loss: 0.9001
183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6247 - loss: 0.9002
186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6247 - loss: 0.9003
189/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6246 - loss: 0.9005
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6246 - loss: 0.9007
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6245 - loss: 0.9008
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6245 - loss: 0.9009
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6245 - loss: 0.9010
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6244 - loss: 0.9011
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6244 - loss: 0.9013
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6244 - loss: 0.9015
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6243 - loss: 0.9016
212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6243 - loss: 0.9018
215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9019
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9020
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9021
224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9022
227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9023
230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9024
233/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9025
236/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9026
239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6241 - loss: 0.9027
242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6241 - loss: 0.9028
245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6241 - loss: 0.9029
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6241 - loss: 0.9030
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6240 - loss: 0.9031
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6240 - loss: 0.9031
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6240 - loss: 0.9032
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6240 - loss: 0.9034
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6239 - loss: 0.9035
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6239 - loss: 0.9036
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6238 - loss: 0.9037
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6238 - loss: 0.9038
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6237 - loss: 0.9040
274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6237 - loss: 0.9040
276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6237 - loss: 0.9041
279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6236 - loss: 0.9042
281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6236 - loss: 0.9043
283/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6236 - loss: 0.9044
286/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6235 - loss: 0.9045
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6234 - loss: 0.9046
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6234 - loss: 0.9047
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6233 - loss: 0.9048
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6233 - loss: 0.9048
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6233 - loss: 0.9049
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6232 - loss: 0.9050
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6232 - loss: 0.9051
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6231 - loss: 0.9052
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6231 - loss: 0.9052
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6231 - loss: 0.9053
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6230 - loss: 0.9053
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6230 - loss: 0.9053
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6230 - loss: 0.9054
324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6229 - loss: 0.9055
327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6229 - loss: 0.9055
330/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6228 - loss: 0.9056
333/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6228 - loss: 0.9057
336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6227 - loss: 0.9057
339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6227 - loss: 0.9058
342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6226 - loss: 0.9059
345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6226 - loss: 0.9060
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6225 - loss: 0.9060
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6224 - loss: 0.9061
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6224 - loss: 0.9062
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6223 - loss: 0.9062
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6223 - loss: 0.9063
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6223 - loss: 0.9064
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9065
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9066
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6221 - loss: 0.9066
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6220 - loss: 0.9067
376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6220 - loss: 0.9068
379/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6219 - loss: 0.9069
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6219 - loss: 0.9069
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6219 - loss: 0.9070
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6218 - loss: 0.9071
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6218 - loss: 0.9072
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6217 - loss: 0.9072
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6216 - loss: 0.9073
397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6216 - loss: 0.9074
400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6216 - loss: 0.9074
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6215 - loss: 0.9075
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6214 - loss: 0.9076
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6214 - loss: 0.9077
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6213 - loss: 0.9078
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6213 - loss: 0.9078
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6212 - loss: 0.9079
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6212 - loss: 0.9080
424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6211 - loss: 0.9080
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6211 - loss: 0.9081
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6210 - loss: 0.9081
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6210 - loss: 0.9082
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6209 - loss: 0.9082
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6209 - loss: 0.9083
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6208 - loss: 0.9084
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6208 - loss: 0.9084
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6208 - loss: 0.9084
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6208 - loss: 0.9085
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6207 - loss: 0.9085
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6207 - loss: 0.9086
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6207 - loss: 0.9086
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6206 - loss: 0.9087
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6206 - loss: 0.9087
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6206 - loss: 0.9088
472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6205 - loss: 0.9089
Epoch 28: val_accuracy did not improve from 0.65240
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6205 - loss: 0.9089 - val_accuracy: 0.5927 - val_loss: 0.9807 - learning_rate: 2.0000e-04
Epoch 29/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 165ms/step - accuracy: 0.5938 - loss: 1.0521
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5514 - loss: 1.0918
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5661 - loss: 1.0529
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5775 - loss: 1.0303
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5858 - loss: 1.0132
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5922 - loss: 0.9995
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5978 - loss: 0.9883
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6003 - loss: 0.9815
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6002 - loss: 0.9779
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5994 - loss: 0.9755
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5990 - loss: 0.9734
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5995 - loss: 0.9705
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6001 - loss: 0.9676
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6007 - loss: 0.9651
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6012 - loss: 0.9634
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6016 - loss: 0.9615
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398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6123 - loss: 0.9255
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6123 - loss: 0.9254
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6123 - loss: 0.9254
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6123 - loss: 0.9253
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6124 - loss: 0.9253
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6124 - loss: 0.9252
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6124 - loss: 0.9252
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6125 - loss: 0.9251
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6125 - loss: 0.9251
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6125 - loss: 0.9250
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6125 - loss: 0.9250
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6125 - loss: 0.9250
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6125 - loss: 0.9249
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6126 - loss: 0.9249
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6126 - loss: 0.9249
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6126 - loss: 0.9248
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6126 - loss: 0.9248
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6126 - loss: 0.9247
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6126 - loss: 0.9247
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9247
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9246
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9246
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9246
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9245
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9245
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9245
Epoch 29: val_accuracy did not improve from 0.65240
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.6127 - loss: 0.9244 - val_accuracy: 0.6303 - val_loss: 0.8999 - learning_rate: 2.0000e-04
Epoch 30/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:24 180ms/step - accuracy: 0.5625 - loss: 0.9556
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6165 - loss: 0.8338
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6268 - loss: 0.8316
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6200 - loss: 0.8398
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6150 - loss: 0.8494
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6148 - loss: 0.8545
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6149 - loss: 0.8571
22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6135 - loss: 0.8619
25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6134 - loss: 0.8655
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6130 - loss: 0.8679
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6123 - loss: 0.8736
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6121 - loss: 0.8762
36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6118 - loss: 0.8800
39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6115 - loss: 0.8829
42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6116 - loss: 0.8852
44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6117 - loss: 0.8865
47/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6117 - loss: 0.8883
50/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6119 - loss: 0.8898
53/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6121 - loss: 0.8911
55/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6123 - loss: 0.8918
58/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6125 - loss: 0.8927
61/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6127 - loss: 0.8938
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6128 - loss: 0.8947
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6129 - loss: 0.8956
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6131 - loss: 0.8964
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6133 - loss: 0.8970
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6135 - loss: 0.8974
77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6136 - loss: 0.8978
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6137 - loss: 0.8981
82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6139 - loss: 0.8986
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6139 - loss: 0.8992
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6140 - loss: 0.8998
91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6142 - loss: 0.9002
93/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6143 - loss: 0.9004
96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6144 - loss: 0.9008
99/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6145 - loss: 0.9011
102/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6146 - loss: 0.9014
105/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6147 - loss: 0.9017
108/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6149 - loss: 0.9020
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6150 - loss: 0.9022
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6151 - loss: 0.9025
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6152 - loss: 0.9027
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6154 - loss: 0.9030
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6155 - loss: 0.9032
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6156 - loss: 0.9034
128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6157 - loss: 0.9035
131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6159 - loss: 0.9037
134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6160 - loss: 0.9038
137/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6161 - loss: 0.9039
140/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6162 - loss: 0.9041
142/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6163 - loss: 0.9043
144/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6163 - loss: 0.9045
147/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6163 - loss: 0.9047
150/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6164 - loss: 0.9049
152/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6164 - loss: 0.9050
155/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6165 - loss: 0.9052
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6165 - loss: 0.9054
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6166 - loss: 0.9056
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6166 - loss: 0.9058
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6167 - loss: 0.9060
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6167 - loss: 0.9061
170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9062
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9064
175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9066
178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9069
181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9072
184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9075
186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9076
188/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9078
191/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9080
194/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9082
197/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6169 - loss: 0.9084
200/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6170 - loss: 0.9085
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6170 - loss: 0.9087
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6171 - loss: 0.9088
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6172 - loss: 0.9089
212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6172 - loss: 0.9090
215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6173 - loss: 0.9091
218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6174 - loss: 0.9092
221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6174 - loss: 0.9092
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6175 - loss: 0.9093
226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6176 - loss: 0.9094
229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6176 - loss: 0.9094
232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6177 - loss: 0.9095
235/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6177 - loss: 0.9096
238/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6178 - loss: 0.9097
241/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6178 - loss: 0.9098
244/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6179 - loss: 0.9099
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6179 - loss: 0.9099
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6179 - loss: 0.9100
253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6180 - loss: 0.9101
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6180 - loss: 0.9102
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6180 - loss: 0.9102
262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9103
265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9104
268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9105
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9105
274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9106
276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9107
279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9107
282/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9108
285/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9109
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6181 - loss: 0.9109
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6181 - loss: 0.9110
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6181 - loss: 0.9110
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6181 - loss: 0.9111
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6181 - loss: 0.9111
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6181 - loss: 0.9112
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9113
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9113
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9114
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9114
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9115
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9115
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9116
325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9116
327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9117
330/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9117
333/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9118
335/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6179 - loss: 0.9118
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9119
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9119
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9120
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9120
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9121
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9121
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9122
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9122
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9122
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9123
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9123
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9123
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9124
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9124
375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9125
378/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9125
381/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9126
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6178 - loss: 0.9126
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6178 - loss: 0.9126
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9127
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9127
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9128
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9128
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9129
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9129
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9129
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9130
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9130
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6176 - loss: 0.9130
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6176 - loss: 0.9131
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6176 - loss: 0.9131
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6176 - loss: 0.9131
424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6176 - loss: 0.9132
426/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6176 - loss: 0.9132
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6176 - loss: 0.9132
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6176 - loss: 0.9132
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6176 - loss: 0.9133
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6176 - loss: 0.9133
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6176 - loss: 0.9133
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6176 - loss: 0.9134
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9134
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9135
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9135
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9136
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9136
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9137
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9137
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6174 - loss: 0.9138
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6174 - loss: 0.9139
Epoch 30: val_accuracy did not improve from 0.65240
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6174 - loss: 0.9140 - val_accuracy: 0.6184 - val_loss: 0.9022 - learning_rate: 2.0000e-04
Epoch 31/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:23 176ms/step - accuracy: 0.5938 - loss: 1.0068
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6094 - loss: 0.9844
6/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.6043 - loss: 0.9937
9/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.6016 - loss: 0.9900
12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6009 - loss: 0.9837
15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6018 - loss: 0.9742
17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6033 - loss: 0.9686
20/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6049 - loss: 0.9613
23/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6062 - loss: 0.9558
26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6073 - loss: 0.9509
28/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6079 - loss: 0.9476
31/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6088 - loss: 0.9427
34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6094 - loss: 0.9388
37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6094 - loss: 0.9359
40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6094 - loss: 0.9334
43/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6095 - loss: 0.9314
46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6095 - loss: 0.9295
49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6096 - loss: 0.9278
52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6096 - loss: 0.9264
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6097 - loss: 0.9250
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6099 - loss: 0.9237
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6102 - loss: 0.9223
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6104 - loss: 0.9214
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6106 - loss: 0.9206
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6106 - loss: 0.9200
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6107 - loss: 0.9193
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6107 - loss: 0.9191
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6109 - loss: 0.9186
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6110 - loss: 0.9183
83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6110 - loss: 0.9182
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6111 - loss: 0.9180
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6111 - loss: 0.9178
90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6112 - loss: 0.9176
93/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6112 - loss: 0.9175
96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6112 - loss: 0.9174
98/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6112 - loss: 0.9174
100/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6111 - loss: 0.9174
102/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6110 - loss: 0.9174
105/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6109 - loss: 0.9174
108/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6108 - loss: 0.9174
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6108 - loss: 0.9174
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6107 - loss: 0.9174
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6108 - loss: 0.9173
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6108 - loss: 0.9173
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6109 - loss: 0.9172
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6109 - loss: 0.9172
129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6109 - loss: 0.9174
132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6109 - loss: 0.9175
135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6108 - loss: 0.9177
138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6108 - loss: 0.9178
141/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6108 - loss: 0.9179
143/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6108 - loss: 0.9180
146/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6108 - loss: 0.9180
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6109 - loss: 0.9181
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6109 - loss: 0.9181
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6109 - loss: 0.9181
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6109 - loss: 0.9181
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6110 - loss: 0.9181
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6110 - loss: 0.9181
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6110 - loss: 0.9181
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6110 - loss: 0.9182
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6110 - loss: 0.9182
175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6110 - loss: 0.9183
178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6110 - loss: 0.9183
181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6110 - loss: 0.9184
184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6110 - loss: 0.9184
186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6110 - loss: 0.9184
189/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6111 - loss: 0.9183
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6111 - loss: 0.9183
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6112 - loss: 0.9182
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6112 - loss: 0.9182
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6113 - loss: 0.9181
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6113 - loss: 0.9181
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6114 - loss: 0.9180
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6115 - loss: 0.9180
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6115 - loss: 0.9179
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6116 - loss: 0.9178
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6117 - loss: 0.9178
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6118 - loss: 0.9177
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6119 - loss: 0.9176
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6120 - loss: 0.9175
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6120 - loss: 0.9175
231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6121 - loss: 0.9174
234/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6122 - loss: 0.9173
237/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6123 - loss: 0.9172
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6123 - loss: 0.9172
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6124 - loss: 0.9171
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6125 - loss: 0.9170
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6125 - loss: 0.9169
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6126 - loss: 0.9168
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6126 - loss: 0.9168
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6127 - loss: 0.9167
260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6127 - loss: 0.9166
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6128 - loss: 0.9166
266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6128 - loss: 0.9165
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6129 - loss: 0.9164
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6129 - loss: 0.9164
275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6130 - loss: 0.9163
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6131 - loss: 0.9163
281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6131 - loss: 0.9162
284/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6132 - loss: 0.9162
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6133 - loss: 0.9161
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6133 - loss: 0.9161
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6134 - loss: 0.9161
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6134 - loss: 0.9160
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6135 - loss: 0.9160
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6135 - loss: 0.9160
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6136 - loss: 0.9160
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6136 - loss: 0.9160
311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6136 - loss: 0.9160
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6137 - loss: 0.9160
317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6137 - loss: 0.9161
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6138 - loss: 0.9161
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6138 - loss: 0.9161
324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6138 - loss: 0.9161
327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6139 - loss: 0.9161
329/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6139 - loss: 0.9161
332/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6139 - loss: 0.9161
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6139 - loss: 0.9161
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6140 - loss: 0.9161
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6140 - loss: 0.9162
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6140 - loss: 0.9162
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6140 - loss: 0.9162
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6141 - loss: 0.9162
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6141 - loss: 0.9162
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6141 - loss: 0.9162
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6141 - loss: 0.9163
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6142 - loss: 0.9163
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6142 - loss: 0.9163
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6142 - loss: 0.9164
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6142 - loss: 0.9164
372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6142 - loss: 0.9164
375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6142 - loss: 0.9165
378/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6143 - loss: 0.9165
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6143 - loss: 0.9165
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6143 - loss: 0.9165
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6143 - loss: 0.9166
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9166
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9166
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9166
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9166
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6145 - loss: 0.9167
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6145 - loss: 0.9167
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6145 - loss: 0.9167
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6146 - loss: 0.9167
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6146 - loss: 0.9167
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6146 - loss: 0.9168
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6146 - loss: 0.9168
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6146 - loss: 0.9168
424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6146 - loss: 0.9168
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6147 - loss: 0.9168
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6147 - loss: 0.9168
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6147 - loss: 0.9168
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6147 - loss: 0.9168
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6148 - loss: 0.9168
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6148 - loss: 0.9168
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6148 - loss: 0.9168
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6148 - loss: 0.9168
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6149 - loss: 0.9168
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6149 - loss: 0.9168
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6149 - loss: 0.9168
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6149 - loss: 0.9168
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6150 - loss: 0.9168
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6150 - loss: 0.9168
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6150 - loss: 0.9168
Epoch 31: val_accuracy did not improve from 0.65240
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6151 - loss: 0.9168 - val_accuracy: 0.6245 - val_loss: 0.9051 - learning_rate: 2.0000e-04
Epoch 32/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:28 187ms/step - accuracy: 0.5625 - loss: 0.9757
3/473 ━━━━━━━━━━━━━━━━━━━━ 14s 31ms/step - accuracy: 0.5677 - loss: 0.9618
5/473 ━━━━━━━━━━━━━━━━━━━━ 16s 35ms/step - accuracy: 0.5831 - loss: 0.9571
8/473 ━━━━━━━━━━━━━━━━━━━━ 13s 29ms/step - accuracy: 0.5899 - loss: 0.9525
11/473 ━━━━━━━━━━━━━━━━━━━━ 11s 26ms/step - accuracy: 0.5915 - loss: 0.9515
13/473 ━━━━━━━━━━━━━━━━━━━━ 11s 26ms/step - accuracy: 0.5911 - loss: 0.9519
16/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5921 - loss: 0.9481
19/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5919 - loss: 0.9459
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368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6167 - loss: 0.9174
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6167 - loss: 0.9174
374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6168 - loss: 0.9174
377/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6168 - loss: 0.9174
380/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6169 - loss: 0.9173
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6169 - loss: 0.9173
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6170 - loss: 0.9173
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6170 - loss: 0.9173
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6170 - loss: 0.9173
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6171 - loss: 0.9173
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6171 - loss: 0.9173
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6171 - loss: 0.9173
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6172 - loss: 0.9173
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6172 - loss: 0.9173
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6172 - loss: 0.9173
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9173
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9173
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6173 - loss: 0.9173
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6173 - loss: 0.9173
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6173 - loss: 0.9173
426/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6173 - loss: 0.9173
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6173 - loss: 0.9173
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6173 - loss: 0.9173
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6175 - loss: 0.9173
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6175 - loss: 0.9173
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6175 - loss: 0.9174
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6175 - loss: 0.9174
Epoch 32: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.
Epoch 32: val_accuracy did not improve from 0.65240
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6175 - loss: 0.9174 - val_accuracy: 0.5919 - val_loss: 1.0019 - learning_rate: 2.0000e-04
Epoch 33/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:39 211ms/step - accuracy: 0.6562 - loss: 0.8991
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6868 - loss: 0.8418
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6874 - loss: 0.8426
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6788 - loss: 0.8512
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6713 - loss: 0.8569
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6618 - loss: 0.8664
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6546 - loss: 0.8745
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6501 - loss: 0.8797
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6474 - loss: 0.8830
26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6451 - loss: 0.8857
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6424 - loss: 0.8884
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6400 - loss: 0.8912
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6381 - loss: 0.8931
38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6362 - loss: 0.8952
41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6345 - loss: 0.8976
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6328 - loss: 0.8999
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6311 - loss: 0.9021
50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6294 - loss: 0.9042
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6283 - loss: 0.9055
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6269 - loss: 0.9074
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6256 - loss: 0.9094
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6245 - loss: 0.9110
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6236 - loss: 0.9127
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6225 - loss: 0.9144
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6215 - loss: 0.9159
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6206 - loss: 0.9171
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6199 - loss: 0.9182
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6192 - loss: 0.9192
82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6187 - loss: 0.9202
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6184 - loss: 0.9207
87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6181 - loss: 0.9214
90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6177 - loss: 0.9221
93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6174 - loss: 0.9226
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6172 - loss: 0.9231
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6170 - loss: 0.9236
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6168 - loss: 0.9240
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6166 - loss: 0.9243
108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6165 - loss: 0.9246
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6164 - loss: 0.9247
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6163 - loss: 0.9248
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6162 - loss: 0.9248
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6162 - loss: 0.9249
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6161 - loss: 0.9249
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6160 - loss: 0.9249
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6160 - loss: 0.9248
129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6161 - loss: 0.9247
131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6161 - loss: 0.9247
133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6161 - loss: 0.9247
136/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6161 - loss: 0.9246
138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6161 - loss: 0.9245
140/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6162 - loss: 0.9244
143/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6162 - loss: 0.9242
146/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6163 - loss: 0.9240
148/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6164 - loss: 0.9239
151/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6165 - loss: 0.9237
153/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6166 - loss: 0.9236
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6167 - loss: 0.9234
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6169 - loss: 0.9231
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6170 - loss: 0.9229
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6171 - loss: 0.9227
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6172 - loss: 0.9225
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6173 - loss: 0.9223
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6174 - loss: 0.9221
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6175 - loss: 0.9220
180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6176 - loss: 0.9217
183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6178 - loss: 0.9216
186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6179 - loss: 0.9214
189/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6180 - loss: 0.9212
192/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6181 - loss: 0.9211
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6182 - loss: 0.9209
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6183 - loss: 0.9207
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6184 - loss: 0.9205
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6185 - loss: 0.9204
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6186 - loss: 0.9202
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6187 - loss: 0.9201
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6188 - loss: 0.9199
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6189 - loss: 0.9198
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6190 - loss: 0.9196
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6191 - loss: 0.9194
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6192 - loss: 0.9192
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6193 - loss: 0.9191
231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6194 - loss: 0.9189
234/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6196 - loss: 0.9187
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6197 - loss: 0.9185
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6198 - loss: 0.9184
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6199 - loss: 0.9182
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6200 - loss: 0.9180
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6201 - loss: 0.9179
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6202 - loss: 0.9177
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6203 - loss: 0.9175
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6204 - loss: 0.9174
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6205 - loss: 0.9173
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6206 - loss: 0.9171
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6207 - loss: 0.9170
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6208 - loss: 0.9169
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6209 - loss: 0.9167
275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6209 - loss: 0.9167
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6210 - loss: 0.9166
281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6211 - loss: 0.9165
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6212 - loss: 0.9164
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6212 - loss: 0.9163
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6213 - loss: 0.9162
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6213 - loss: 0.9161
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6214 - loss: 0.9161
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6214 - loss: 0.9160
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6215 - loss: 0.9160
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6215 - loss: 0.9160
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6216 - loss: 0.9159
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6216 - loss: 0.9159
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6216 - loss: 0.9159
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6217 - loss: 0.9159
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6217 - loss: 0.9159
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6217 - loss: 0.9159
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6218 - loss: 0.9158
325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6218 - loss: 0.9158
328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6218 - loss: 0.9158
331/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6219 - loss: 0.9158
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6219 - loss: 0.9158
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6219 - loss: 0.9158
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6219 - loss: 0.9158
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6220 - loss: 0.9158
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6220 - loss: 0.9157
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6221 - loss: 0.9157
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6221 - loss: 0.9157
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6221 - loss: 0.9157
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6221 - loss: 0.9157
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6221 - loss: 0.9157
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9157
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9157
369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9157
372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9157
375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9156
378/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9156
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6223 - loss: 0.9156
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6223 - loss: 0.9156
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6223 - loss: 0.9156
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6223 - loss: 0.9156
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6223 - loss: 0.9155
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6224 - loss: 0.9155
397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6224 - loss: 0.9155
400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6224 - loss: 0.9155
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6224 - loss: 0.9155
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6224 - loss: 0.9154
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6224 - loss: 0.9154
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6225 - loss: 0.9154
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6225 - loss: 0.9154
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6225 - loss: 0.9153
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6225 - loss: 0.9153
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6225 - loss: 0.9153
424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6225 - loss: 0.9153
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9153
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9152
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9152
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9152
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9152
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9151
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9151
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9151
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6227 - loss: 0.9151
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6227 - loss: 0.9150
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6227 - loss: 0.9150
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6227 - loss: 0.9150
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6227 - loss: 0.9149
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6227 - loss: 0.9149
471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6228 - loss: 0.9148
Epoch 33: val_accuracy did not improve from 0.65240
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6228 - loss: 0.9148 - val_accuracy: 0.6432 - val_loss: 0.8618 - learning_rate: 4.0000e-05
Epoch 34/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 164ms/step - accuracy: 0.5625 - loss: 0.9791
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5957 - loss: 0.9582
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5933 - loss: 0.9645
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5945 - loss: 0.9612
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5947 - loss: 0.9603
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5971 - loss: 0.9575
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6005 - loss: 0.9542
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6045 - loss: 0.9491
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6067 - loss: 0.9464
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6088 - loss: 0.9438
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6102 - loss: 0.9411
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6111 - loss: 0.9391
36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6114 - loss: 0.9376
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6114 - loss: 0.9368
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6114 - loss: 0.9364
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6115 - loss: 0.9360
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6117 - loss: 0.9351
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6119 - loss: 0.9342
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6123 - loss: 0.9330
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6128 - loss: 0.9318
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6134 - loss: 0.9305
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6140 - loss: 0.9293
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6142 - loss: 0.9286
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6146 - loss: 0.9276
68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6150 - loss: 0.9266
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6154 - loss: 0.9259
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6157 - loss: 0.9253
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6161 - loss: 0.9243
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6166 - loss: 0.9234
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6171 - loss: 0.9223
83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6174 - loss: 0.9216
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6177 - loss: 0.9210
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6180 - loss: 0.9201
91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6183 - loss: 0.9192
94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6186 - loss: 0.9185
97/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6188 - loss: 0.9179
100/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6189 - loss: 0.9173
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6191 - loss: 0.9167
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6192 - loss: 0.9163
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6193 - loss: 0.9160
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6194 - loss: 0.9154
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6195 - loss: 0.9149
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6196 - loss: 0.9144
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Epoch 34: val_accuracy did not improve from 0.65240
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6241 - loss: 0.9030 - val_accuracy: 0.6409 - val_loss: 0.8619 - learning_rate: 4.0000e-05
Epoch 34: early stopping
Restoring model weights from the end of the best epoch: 27.
Plotting the Training and Validation Accuracies¶
plt.plot(history_vgg.history["accuracy"])
plt.plot(history_vgg.history["val_accuracy"])
plt.title("VGG16 Model accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train", "validation"], loc="upper left")
plt.show()
Evaluating the VGG16 model¶
# Calculate the number of steps for the entire test set to be processed
test_steps = test_generator_vgg16.samples // batch_size
# If the number of samples isn't a multiple of the batch size,
# you have one more batch with the remaining samples
if test_generator.samples % batch_size > 0:
test_steps += 1
# Evaluating the model on the test set
evaluation_results = new_vgg16_model.evaluate(test_generator_vgg16, steps=test_steps)
print(f"Loss: {evaluation_results[0]}, Accuracy: {evaluation_results[1]}")
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.8750 - loss: 0.5965
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.6979 - loss: 0.7874
Loss: 0.8395289778709412, Accuracy: 0.65625
Plotting Confusion Matrix¶
pred_probabilities = new_vgg16_model.predict(test_generator_vgg16, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)
# Getting the true labels from the generator
y_true = test_generator_vgg16.classes
# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))
# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("VGG16 Model Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 1s 461ms/step
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step
precision recall f1-score support
happy 0.64 0.88 0.74 32
neutral 0.54 0.44 0.48 32
sad 0.61 0.62 0.62 32
surprise 0.88 0.69 0.77 32
accuracy 0.66 128
macro avg 0.67 0.66 0.65 128
weighted avg 0.67 0.66 0.65 128
Think About It:
- What do you infer from the general trend in the training performance?
- Is the training accuracy consistently improving?
- Is the validation accuracy also improving similarly?
Observations and Insights:
- Utilizing the VGG16 architecture, the model has a total of 1,735,488; however, they are not trainable due to freezing the earlier layers.
- The model achieved an accuracy of 65.62%, which is less compared to the earlier CNN models, implying that the transfer learning approach may not have been as effective in this particular case.
- In terms of individual class performance, the model performed best on the 'happy' and 'surprise' emotions with an f1-score of 0.77 and 0.74, showing strong recognition capabilities for these emotions.
- The class 'sad' saw moderate f1-score of 0.62, whereas 'neutral' had the lowest f1-score of 0.48, indicating that the model struggles more with neutral expressions and general differentiation between these emotions.
This analysis suggests that while leveraging a pre-trained network like VGG16 brings in advanced feature detection capabilities, for the specific case of small grayscale images representing facial emotions, the intricate features learned by VGG16 from large-scale colored image datasets may not fully translate, thereby not offering a substantial advantage over simpler, tailored CNN architectures.
Note: You can even go back and build your own architecture on top of the VGG16 Transfer layer and see if you can improve the performance
- We have tried to improve the model by changing the learning rate, adding more layers, changing the number of units in the layers, and changing the optimizer.
ResNet V2 Model¶
backend.clear_session()
# Fixing the seed for random number generators so that we can ensure we receive the same output everytime
np.random.seed(42)
random.seed(42)
tf.random.set_seed(42)
resnet_model = ResNet50V2(weights="imagenet", include_top=False, input_shape=(img_width, img_height, color_layers))
resnet_model.summary()
Model: "resnet50v2"
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ │ input_layer │ (None, 48, 48, 3) │ 0 │ - │ │ (InputLayer) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv1_pad │ (None, 54, 54, 3) │ 0 │ input_layer[0][0] │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv1_conv (Conv2D) │ (None, 24, 24, │ 9,472 │ conv1_pad[0][0] │ │ │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ pool1_pad │ (None, 26, 26, │ 0 │ conv1_conv[0][0] │ │ (ZeroPadding2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ pool1_pool │ (None, 12, 12, │ 0 │ pool1_pad[0][0] │ │ (MaxPooling2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block1_preac… │ (None, 12, 12, │ 256 │ pool1_pool[0][0] │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block1_preac… │ (None, 12, 12, │ 0 │ conv2_block1_pre… │ │ (Activation) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block1_1_conv │ (None, 12, 12, │ 4,096 │ conv2_block1_pre… │ │ (Conv2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block1_1_bn │ (None, 12, 12, │ 256 │ conv2_block1_1_c… │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block1_1_relu │ (None, 12, 12, │ 0 │ conv2_block1_1_b… │ │ (Activation) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block1_2_pad │ (None, 14, 14, │ 0 │ conv2_block1_1_r… │ │ (ZeroPadding2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block1_2_conv │ (None, 12, 12, │ 36,864 │ conv2_block1_2_p… │ │ (Conv2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block1_2_bn │ (None, 12, 12, │ 256 │ conv2_block1_2_c… │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block1_2_relu │ (None, 12, 12, │ 0 │ conv2_block1_2_b… │ │ (Activation) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block1_0_conv │ (None, 12, 12, │ 16,640 │ conv2_block1_pre… │ │ (Conv2D) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block1_3_conv │ (None, 12, 12, │ 16,640 │ conv2_block1_2_r… │ │ (Conv2D) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block1_out │ (None, 12, 12, │ 0 │ conv2_block1_0_c… │ │ (Add) │ 256) │ │ conv2_block1_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block2_preac… │ (None, 12, 12, │ 1,024 │ conv2_block1_out… │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block2_preac… │ (None, 12, 12, │ 0 │ conv2_block2_pre… │ │ (Activation) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block2_1_conv │ (None, 12, 12, │ 16,384 │ conv2_block2_pre… │ │ (Conv2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block2_1_bn │ (None, 12, 12, │ 256 │ conv2_block2_1_c… │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block2_1_relu │ (None, 12, 12, │ 0 │ conv2_block2_1_b… │ │ (Activation) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block2_2_pad │ (None, 14, 14, │ 0 │ conv2_block2_1_r… │ │ (ZeroPadding2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block2_2_conv │ (None, 12, 12, │ 36,864 │ conv2_block2_2_p… │ │ (Conv2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block2_2_bn │ (None, 12, 12, │ 256 │ conv2_block2_2_c… │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block2_2_relu │ (None, 12, 12, │ 0 │ conv2_block2_2_b… │ │ (Activation) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block2_3_conv │ (None, 12, 12, │ 16,640 │ conv2_block2_2_r… │ │ (Conv2D) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block2_out │ (None, 12, 12, │ 0 │ conv2_block1_out… │ │ (Add) │ 256) │ │ conv2_block2_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block3_preac… │ (None, 12, 12, │ 1,024 │ conv2_block2_out… │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block3_preac… │ (None, 12, 12, │ 0 │ conv2_block3_pre… │ │ (Activation) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block3_1_conv │ (None, 12, 12, │ 16,384 │ conv2_block3_pre… │ │ (Conv2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block3_1_bn │ (None, 12, 12, │ 256 │ conv2_block3_1_c… │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block3_1_relu │ (None, 12, 12, │ 0 │ conv2_block3_1_b… │ │ (Activation) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block3_2_pad │ (None, 14, 14, │ 0 │ conv2_block3_1_r… │ │ (ZeroPadding2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block3_2_conv │ (None, 6, 6, 64) │ 36,864 │ conv2_block3_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block3_2_bn │ (None, 6, 6, 64) │ 256 │ conv2_block3_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block3_2_relu │ (None, 6, 6, 64) │ 0 │ conv2_block3_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ max_pooling2d │ (None, 6, 6, 256) │ 0 │ conv2_block2_out… │ │ (MaxPooling2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block3_3_conv │ (None, 6, 6, 256) │ 16,640 │ conv2_block3_2_r… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2_block3_out │ (None, 6, 6, 256) │ 0 │ max_pooling2d[0]… │ │ (Add) │ │ │ conv2_block3_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block1_preac… │ (None, 6, 6, 256) │ 1,024 │ conv2_block3_out… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block1_preac… │ (None, 6, 6, 256) │ 0 │ conv3_block1_pre… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block1_1_conv │ (None, 6, 6, 128) │ 32,768 │ conv3_block1_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block1_1_bn │ (None, 6, 6, 128) │ 512 │ conv3_block1_1_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block1_1_relu │ (None, 6, 6, 128) │ 0 │ conv3_block1_1_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block1_2_pad │ (None, 8, 8, 128) │ 0 │ conv3_block1_1_r… │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block1_2_conv │ (None, 6, 6, 128) │ 147,456 │ conv3_block1_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block1_2_bn │ (None, 6, 6, 128) │ 512 │ conv3_block1_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block1_2_relu │ (None, 6, 6, 128) │ 0 │ conv3_block1_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block1_0_conv │ (None, 6, 6, 512) │ 131,584 │ conv3_block1_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block1_3_conv │ (None, 6, 6, 512) │ 66,048 │ conv3_block1_2_r… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block1_out │ (None, 6, 6, 512) │ 0 │ conv3_block1_0_c… │ │ (Add) │ │ │ conv3_block1_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block2_preac… │ (None, 6, 6, 512) │ 2,048 │ conv3_block1_out… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block2_preac… │ (None, 6, 6, 512) │ 0 │ conv3_block2_pre… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block2_1_conv │ (None, 6, 6, 128) │ 65,536 │ conv3_block2_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block2_1_bn │ (None, 6, 6, 128) │ 512 │ conv3_block2_1_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block2_1_relu │ (None, 6, 6, 128) │ 0 │ conv3_block2_1_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block2_2_pad │ (None, 8, 8, 128) │ 0 │ conv3_block2_1_r… │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block2_2_conv │ (None, 6, 6, 128) │ 147,456 │ conv3_block2_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block2_2_bn │ (None, 6, 6, 128) │ 512 │ conv3_block2_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block2_2_relu │ (None, 6, 6, 128) │ 0 │ conv3_block2_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block2_3_conv │ (None, 6, 6, 512) │ 66,048 │ conv3_block2_2_r… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block2_out │ (None, 6, 6, 512) │ 0 │ conv3_block1_out… │ │ (Add) │ │ │ conv3_block2_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block3_preac… │ (None, 6, 6, 512) │ 2,048 │ conv3_block2_out… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block3_preac… │ (None, 6, 6, 512) │ 0 │ conv3_block3_pre… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block3_1_conv │ (None, 6, 6, 128) │ 65,536 │ conv3_block3_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block3_1_bn │ (None, 6, 6, 128) │ 512 │ conv3_block3_1_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block3_1_relu │ (None, 6, 6, 128) │ 0 │ conv3_block3_1_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block3_2_pad │ (None, 8, 8, 128) │ 0 │ conv3_block3_1_r… │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block3_2_conv │ (None, 6, 6, 128) │ 147,456 │ conv3_block3_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block3_2_bn │ (None, 6, 6, 128) │ 512 │ conv3_block3_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block3_2_relu │ (None, 6, 6, 128) │ 0 │ conv3_block3_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block3_3_conv │ (None, 6, 6, 512) │ 66,048 │ conv3_block3_2_r… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block3_out │ (None, 6, 6, 512) │ 0 │ conv3_block2_out… │ │ (Add) │ │ │ conv3_block3_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block4_preac… │ (None, 6, 6, 512) │ 2,048 │ conv3_block3_out… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block4_preac… │ (None, 6, 6, 512) │ 0 │ conv3_block4_pre… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block4_1_conv │ (None, 6, 6, 128) │ 65,536 │ conv3_block4_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block4_1_bn │ (None, 6, 6, 128) │ 512 │ conv3_block4_1_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block4_1_relu │ (None, 6, 6, 128) │ 0 │ conv3_block4_1_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block4_2_pad │ (None, 8, 8, 128) │ 0 │ conv3_block4_1_r… │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block4_2_conv │ (None, 3, 3, 128) │ 147,456 │ conv3_block4_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block4_2_bn │ (None, 3, 3, 128) │ 512 │ conv3_block4_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block4_2_relu │ (None, 3, 3, 128) │ 0 │ conv3_block4_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ max_pooling2d_1 │ (None, 3, 3, 512) │ 0 │ conv3_block3_out… │ │ (MaxPooling2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block4_3_conv │ (None, 3, 3, 512) │ 66,048 │ conv3_block4_2_r… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv3_block4_out │ (None, 3, 3, 512) │ 0 │ max_pooling2d_1[… │ │ (Add) │ │ │ conv3_block4_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block1_preac… │ (None, 3, 3, 512) │ 2,048 │ conv3_block4_out… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block1_preac… │ (None, 3, 3, 512) │ 0 │ conv4_block1_pre… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block1_1_conv │ (None, 3, 3, 256) │ 131,072 │ conv4_block1_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block1_1_bn │ (None, 3, 3, 256) │ 1,024 │ conv4_block1_1_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block1_1_relu │ (None, 3, 3, 256) │ 0 │ conv4_block1_1_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block1_2_pad │ (None, 5, 5, 256) │ 0 │ conv4_block1_1_r… │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block1_2_conv │ (None, 3, 3, 256) │ 589,824 │ conv4_block1_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block1_2_bn │ (None, 3, 3, 256) │ 1,024 │ conv4_block1_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block1_2_relu │ (None, 3, 3, 256) │ 0 │ conv4_block1_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block1_0_conv │ (None, 3, 3, │ 525,312 │ conv4_block1_pre… │ │ (Conv2D) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block1_3_conv │ (None, 3, 3, │ 263,168 │ conv4_block1_2_r… │ │ (Conv2D) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block1_out │ (None, 3, 3, │ 0 │ conv4_block1_0_c… │ │ (Add) │ 1024) │ │ conv4_block1_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block2_preac… │ (None, 3, 3, │ 4,096 │ conv4_block1_out… │ │ (BatchNormalizatio… │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block2_preac… │ (None, 3, 3, │ 0 │ conv4_block2_pre… │ │ (Activation) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block2_1_conv │ (None, 3, 3, 256) │ 262,144 │ conv4_block2_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block2_1_bn │ (None, 3, 3, 256) │ 1,024 │ conv4_block2_1_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block2_1_relu │ (None, 3, 3, 256) │ 0 │ conv4_block2_1_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block2_2_pad │ (None, 5, 5, 256) │ 0 │ conv4_block2_1_r… │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block2_2_conv │ (None, 3, 3, 256) │ 589,824 │ conv4_block2_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block2_2_bn │ (None, 3, 3, 256) │ 1,024 │ conv4_block2_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block2_2_relu │ (None, 3, 3, 256) │ 0 │ conv4_block2_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block2_3_conv │ (None, 3, 3, │ 263,168 │ conv4_block2_2_r… │ │ (Conv2D) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block2_out │ (None, 3, 3, │ 0 │ conv4_block1_out… │ │ (Add) │ 1024) │ │ conv4_block2_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block3_preac… │ (None, 3, 3, │ 4,096 │ conv4_block2_out… │ │ (BatchNormalizatio… │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block3_preac… │ (None, 3, 3, │ 0 │ conv4_block3_pre… │ │ (Activation) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block3_1_conv │ (None, 3, 3, 256) │ 262,144 │ conv4_block3_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block3_1_bn │ (None, 3, 3, 256) │ 1,024 │ conv4_block3_1_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block3_1_relu │ (None, 3, 3, 256) │ 0 │ conv4_block3_1_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block3_2_pad │ (None, 5, 5, 256) │ 0 │ conv4_block3_1_r… │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block3_2_conv │ (None, 3, 3, 256) │ 589,824 │ conv4_block3_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block3_2_bn │ (None, 3, 3, 256) │ 1,024 │ conv4_block3_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block3_2_relu │ (None, 3, 3, 256) │ 0 │ conv4_block3_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block3_3_conv │ (None, 3, 3, │ 263,168 │ conv4_block3_2_r… │ │ (Conv2D) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block3_out │ (None, 3, 3, │ 0 │ conv4_block2_out… │ │ (Add) │ 1024) │ │ conv4_block3_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block4_preac… │ (None, 3, 3, │ 4,096 │ conv4_block3_out… │ │ (BatchNormalizatio… │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block4_preac… │ (None, 3, 3, │ 0 │ conv4_block4_pre… │ │ (Activation) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block4_1_conv │ (None, 3, 3, 256) │ 262,144 │ conv4_block4_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block4_1_bn │ (None, 3, 3, 256) │ 1,024 │ conv4_block4_1_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block4_1_relu │ (None, 3, 3, 256) │ 0 │ conv4_block4_1_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block4_2_pad │ (None, 5, 5, 256) │ 0 │ conv4_block4_1_r… │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block4_2_conv │ (None, 3, 3, 256) │ 589,824 │ conv4_block4_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block4_2_bn │ (None, 3, 3, 256) │ 1,024 │ conv4_block4_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block4_2_relu │ (None, 3, 3, 256) │ 0 │ conv4_block4_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block4_3_conv │ (None, 3, 3, │ 263,168 │ conv4_block4_2_r… │ │ (Conv2D) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block4_out │ (None, 3, 3, │ 0 │ conv4_block3_out… │ │ (Add) │ 1024) │ │ conv4_block4_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block5_preac… │ (None, 3, 3, │ 4,096 │ conv4_block4_out… │ │ (BatchNormalizatio… │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block5_preac… │ (None, 3, 3, │ 0 │ conv4_block5_pre… │ │ (Activation) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block5_1_conv │ (None, 3, 3, 256) │ 262,144 │ conv4_block5_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block5_1_bn │ (None, 3, 3, 256) │ 1,024 │ conv4_block5_1_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block5_1_relu │ (None, 3, 3, 256) │ 0 │ conv4_block5_1_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block5_2_pad │ (None, 5, 5, 256) │ 0 │ conv4_block5_1_r… │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block5_2_conv │ (None, 3, 3, 256) │ 589,824 │ conv4_block5_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block5_2_bn │ (None, 3, 3, 256) │ 1,024 │ conv4_block5_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block5_2_relu │ (None, 3, 3, 256) │ 0 │ conv4_block5_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block5_3_conv │ (None, 3, 3, │ 263,168 │ conv4_block5_2_r… │ │ (Conv2D) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block5_out │ (None, 3, 3, │ 0 │ conv4_block4_out… │ │ (Add) │ 1024) │ │ conv4_block5_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block6_preac… │ (None, 3, 3, │ 4,096 │ conv4_block5_out… │ │ (BatchNormalizatio… │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block6_preac… │ (None, 3, 3, │ 0 │ conv4_block6_pre… │ │ (Activation) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block6_1_conv │ (None, 3, 3, 256) │ 262,144 │ conv4_block6_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block6_1_bn │ (None, 3, 3, 256) │ 1,024 │ conv4_block6_1_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block6_1_relu │ (None, 3, 3, 256) │ 0 │ conv4_block6_1_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block6_2_pad │ (None, 5, 5, 256) │ 0 │ conv4_block6_1_r… │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block6_2_conv │ (None, 2, 2, 256) │ 589,824 │ conv4_block6_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block6_2_bn │ (None, 2, 2, 256) │ 1,024 │ conv4_block6_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block6_2_relu │ (None, 2, 2, 256) │ 0 │ conv4_block6_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ max_pooling2d_2 │ (None, 2, 2, │ 0 │ conv4_block5_out… │ │ (MaxPooling2D) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block6_3_conv │ (None, 2, 2, │ 263,168 │ conv4_block6_2_r… │ │ (Conv2D) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv4_block6_out │ (None, 2, 2, │ 0 │ max_pooling2d_2[… │ │ (Add) │ 1024) │ │ conv4_block6_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block1_preac… │ (None, 2, 2, │ 4,096 │ conv4_block6_out… │ │ (BatchNormalizatio… │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block1_preac… │ (None, 2, 2, │ 0 │ conv5_block1_pre… │ │ (Activation) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block1_1_conv │ (None, 2, 2, 512) │ 524,288 │ conv5_block1_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block1_1_bn │ (None, 2, 2, 512) │ 2,048 │ conv5_block1_1_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block1_1_relu │ (None, 2, 2, 512) │ 0 │ conv5_block1_1_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block1_2_pad │ (None, 4, 4, 512) │ 0 │ conv5_block1_1_r… │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block1_2_conv │ (None, 2, 2, 512) │ 2,359,296 │ conv5_block1_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block1_2_bn │ (None, 2, 2, 512) │ 2,048 │ conv5_block1_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block1_2_relu │ (None, 2, 2, 512) │ 0 │ conv5_block1_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block1_0_conv │ (None, 2, 2, │ 2,099,200 │ conv5_block1_pre… │ │ (Conv2D) │ 2048) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block1_3_conv │ (None, 2, 2, │ 1,050,624 │ conv5_block1_2_r… │ │ (Conv2D) │ 2048) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block1_out │ (None, 2, 2, │ 0 │ conv5_block1_0_c… │ │ (Add) │ 2048) │ │ conv5_block1_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block2_preac… │ (None, 2, 2, │ 8,192 │ conv5_block1_out… │ │ (BatchNormalizatio… │ 2048) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block2_preac… │ (None, 2, 2, │ 0 │ conv5_block2_pre… │ │ (Activation) │ 2048) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block2_1_conv │ (None, 2, 2, 512) │ 1,048,576 │ conv5_block2_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block2_1_bn │ (None, 2, 2, 512) │ 2,048 │ conv5_block2_1_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block2_1_relu │ (None, 2, 2, 512) │ 0 │ conv5_block2_1_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block2_2_pad │ (None, 4, 4, 512) │ 0 │ conv5_block2_1_r… │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block2_2_conv │ (None, 2, 2, 512) │ 2,359,296 │ conv5_block2_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block2_2_bn │ (None, 2, 2, 512) │ 2,048 │ conv5_block2_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block2_2_relu │ (None, 2, 2, 512) │ 0 │ conv5_block2_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block2_3_conv │ (None, 2, 2, │ 1,050,624 │ conv5_block2_2_r… │ │ (Conv2D) │ 2048) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block2_out │ (None, 2, 2, │ 0 │ conv5_block1_out… │ │ (Add) │ 2048) │ │ conv5_block2_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block3_preac… │ (None, 2, 2, │ 8,192 │ conv5_block2_out… │ │ (BatchNormalizatio… │ 2048) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block3_preac… │ (None, 2, 2, │ 0 │ conv5_block3_pre… │ │ (Activation) │ 2048) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block3_1_conv │ (None, 2, 2, 512) │ 1,048,576 │ conv5_block3_pre… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block3_1_bn │ (None, 2, 2, 512) │ 2,048 │ conv5_block3_1_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block3_1_relu │ (None, 2, 2, 512) │ 0 │ conv5_block3_1_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block3_2_pad │ (None, 4, 4, 512) │ 0 │ conv5_block3_1_r… │ │ (ZeroPadding2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block3_2_conv │ (None, 2, 2, 512) │ 2,359,296 │ conv5_block3_2_p… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block3_2_bn │ (None, 2, 2, 512) │ 2,048 │ conv5_block3_2_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block3_2_relu │ (None, 2, 2, 512) │ 0 │ conv5_block3_2_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block3_3_conv │ (None, 2, 2, │ 1,050,624 │ conv5_block3_2_r… │ │ (Conv2D) │ 2048) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv5_block3_out │ (None, 2, 2, │ 0 │ conv5_block2_out… │ │ (Add) │ 2048) │ │ conv5_block3_3_c… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ post_bn │ (None, 2, 2, │ 8,192 │ conv5_block3_out… │ │ (BatchNormalizatio… │ 2048) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ post_relu │ (None, 2, 2, │ 0 │ post_bn[0][0] │ │ (Activation) │ 2048) │ │ │ └─────────────────────┴───────────────────┴────────────┴───────────────────┘
Total params: 23,564,800 (89.89 MB)
Trainable params: 23,519,360 (89.72 MB)
Non-trainable params: 45,440 (177.50 KB)
Model Building¶
- Import Resnet v2 upto the layer of your choice and add Fully Connected layers on top of it.
# Define a new model that cuts ResNet50V2 at the 'conv3_block4_out' layer
model_output = resnet_model.get_layer("conv3_block4_out").output
cut_model = Model(inputs=resnet_model.input, outputs=model_output)
# Freezing the layers
for layer in resnet_model.layers:
layer.trainable = False
new_resnet_model = Sequential()
new_resnet_model.add(cut_model)
# Reduces each feature map to a single value by averaging all elements
new_resnet_model.add(GlobalAveragePooling2D())
# Adding full connected layers
new_resnet_model.add(Dense(256, activation="relu"))
# Adding output layer
new_resnet_model.add(Dense(4, activation="softmax"))
# Using Adam Optimizer
optimizer = Adam(learning_rate=0.001)
Compiling and Training the Model¶
new_resnet_model.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])
new_resnet_model.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ functional_1 (Functional) │ ? │ 1,453,568 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ global_average_pooling2d │ ? │ 0 (unbuilt) │ │ (GlobalAveragePooling2D) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense (Dense) │ ? │ 0 (unbuilt) │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_1 (Dense) │ ? │ 0 (unbuilt) │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 1,453,568 (5.54 MB)
Trainable params: 0 (0.00 B)
Non-trainable params: 1,453,568 (5.54 MB)
# Get the current time
current_time = datetime.now().strftime("%Y%m%d-%H%M%S")
# Set up Early Stopping with a patience 7 but acting after at least 20 epochs
delayed_early_stopping = DelayedEarlyStopping(
monitor="val_loss", patience=7, verbose=1, restore_best_weights=True, start_epoch=20
)
# Define the learning rate scheduler callback
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, patience=5, min_lr=0.00001, verbose=1)
mc = ModelCheckpoint(
f"{results_path}/best_model_resnet_{current_time}.keras",
monitor="val_accuracy",
mode="max",
verbose=1,
save_best_only=True,
)
# Fitting the model with 40 epochs and using validation set
history_resnet = new_resnet_model.fit(
train_generator_resnet,
epochs=40,
validation_data=validation_generator_resnet,
callbacks=[reduce_lr, mc, delayed_early_stopping],
)
Epoch 1/40
/home/iamtxena/sandbox/mit-ai/my_env/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored. self._warn_if_super_not_called()
I0000 00:00:1712794843.982897 1499815 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_3697', 4 bytes spill stores, 4 bytes spill loads
I0000 00:00:1712794844.285459 1499816 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_3697', 32 bytes spill stores, 32 bytes spill loads I0000 00:00:1712794844.474295 1499820 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_3697', 40 bytes spill stores, 40 bytes spill loads
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Epoch 1: val_accuracy improved from -inf to 0.48081, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 21s 34ms/step - accuracy: 0.4011 - loss: 1.3650 - val_accuracy: 0.4808 - val_loss: 1.1593 - learning_rate: 0.0010
Epoch 2/40
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9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5106 - loss: 1.1438
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Epoch 2: val_accuracy improved from 0.48081 to 0.54330, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.4816 - loss: 1.1602 - val_accuracy: 0.5433 - val_loss: 1.0592 - learning_rate: 0.0010
Epoch 3/40
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419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4962 - loss: 1.1410
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4962 - loss: 1.1409
424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4962 - loss: 1.1409
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4962 - loss: 1.1409
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4962 - loss: 1.1409
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4962 - loss: 1.1409
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4962 - loss: 1.1409
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4962 - loss: 1.1409
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4960 - loss: 1.1409
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4960 - loss: 1.1409
Epoch 3: val_accuracy did not improve from 0.54330
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.4960 - loss: 1.1408 - val_accuracy: 0.5252 - val_loss: 1.0921 - learning_rate: 0.0010
Epoch 4/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:21 174ms/step - accuracy: 0.5625 - loss: 1.0699
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.4714 - loss: 1.1716
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4815 - loss: 1.1635
9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.4851 - loss: 1.1602
11/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.4891 - loss: 1.1554
14/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.4919 - loss: 1.1507
17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.4929 - loss: 1.1481
20/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.4944 - loss: 1.1441
23/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.4960 - loss: 1.1395
26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.4963 - loss: 1.1363
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4964 - loss: 1.1350
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4967 - loss: 1.1343
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4969 - loss: 1.1340
38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4969 - loss: 1.1342
40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4970 - loss: 1.1340
42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.4972 - loss: 1.1337
45/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.4975 - loss: 1.1332
48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4981 - loss: 1.1326
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.4988 - loss: 1.1320
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.4998 - loss: 1.1311
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5006 - loss: 1.1303
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5015 - loss: 1.1296
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5023 - loss: 1.1287
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5030 - loss: 1.1279
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5037 - loss: 1.1271
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5043 - loss: 1.1264
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5048 - loss: 1.1258
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5051 - loss: 1.1253
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5052 - loss: 1.1251
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5053 - loss: 1.1248
88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5054 - loss: 1.1245
91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5054 - loss: 1.1243
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5055 - loss: 1.1243
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5054 - loss: 1.1242
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5054 - loss: 1.1242
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5054 - loss: 1.1241
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5055 - loss: 1.1240
108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5055 - loss: 1.1239
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5055 - loss: 1.1238
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5055 - loss: 1.1237
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5055 - loss: 1.1236
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5054 - loss: 1.1236
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5054 - loss: 1.1235
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5054 - loss: 1.1235
127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5054 - loss: 1.1234
130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5055 - loss: 1.1232
133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5056 - loss: 1.1230
136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5057 - loss: 1.1227
139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5058 - loss: 1.1225
142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5060 - loss: 1.1223
145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5061 - loss: 1.1220
148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5062 - loss: 1.1218
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5063 - loss: 1.1216
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5064 - loss: 1.1214
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5065 - loss: 1.1213
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5066 - loss: 1.1211
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5067 - loss: 1.1209
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5068 - loss: 1.1208
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5069 - loss: 1.1206
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5070 - loss: 1.1204
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5070 - loss: 1.1203
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5071 - loss: 1.1201
180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5072 - loss: 1.1199
183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5073 - loss: 1.1198
186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5073 - loss: 1.1197
189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5074 - loss: 1.1196
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5074 - loss: 1.1195
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5074 - loss: 1.1194
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5075 - loss: 1.1193
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5075 - loss: 1.1192
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5075 - loss: 1.1191
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5075 - loss: 1.1191
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5075 - loss: 1.1190
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5075 - loss: 1.1190
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5075 - loss: 1.1190
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5076 - loss: 1.1189
221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5076 - loss: 1.1189
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5076 - loss: 1.1189
226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5076 - loss: 1.1188
229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5076 - loss: 1.1188
232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5076 - loss: 1.1187
235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5076 - loss: 1.1187
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5076 - loss: 1.1186
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5076 - loss: 1.1186
244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5076 - loss: 1.1186
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5076 - loss: 1.1185
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5076 - loss: 1.1185
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5077 - loss: 1.1185
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5077 - loss: 1.1185
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5077 - loss: 1.1184
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5077 - loss: 1.1184
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5077 - loss: 1.1184
266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5077 - loss: 1.1184
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5077 - loss: 1.1184
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5077 - loss: 1.1184
274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5077 - loss: 1.1184
277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5077 - loss: 1.1185
280/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5077 - loss: 1.1185
283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5077 - loss: 1.1185
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5077 - loss: 1.1186
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5077 - loss: 1.1186
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5077 - loss: 1.1186
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5077 - loss: 1.1186
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5076 - loss: 1.1186
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5076 - loss: 1.1187
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Epoch 4: val_accuracy improved from 0.54330 to 0.55375, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5073 - loss: 1.1203 - val_accuracy: 0.5537 - val_loss: 1.0311 - learning_rate: 0.0010
Epoch 5/40
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184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5023 - loss: 1.1268
187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5022 - loss: 1.1270
190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5021 - loss: 1.1271
193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5020 - loss: 1.1273
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5019 - loss: 1.1273
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5018 - loss: 1.1274
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5017 - loss: 1.1275
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5016 - loss: 1.1276
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5015 - loss: 1.1277
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5015 - loss: 1.1277
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5014 - loss: 1.1277
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5014 - loss: 1.1278
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5013 - loss: 1.1278
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5013 - loss: 1.1278
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5012 - loss: 1.1279
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5012 - loss: 1.1279
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5012 - loss: 1.1279
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5011 - loss: 1.1279
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5011 - loss: 1.1279
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5011 - loss: 1.1280
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5011 - loss: 1.1280
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5011 - loss: 1.1280
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5010 - loss: 1.1280
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5010 - loss: 1.1279
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5010 - loss: 1.1279
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5010 - loss: 1.1279
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5010 - loss: 1.1279
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5010 - loss: 1.1279
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5010 - loss: 1.1279
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5010 - loss: 1.1279
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5010 - loss: 1.1279
276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5010 - loss: 1.1278
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5010 - loss: 1.1278
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5010 - loss: 1.1278
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5010 - loss: 1.1278
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5010 - loss: 1.1278
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5010 - loss: 1.1277
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5010 - loss: 1.1277
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5011 - loss: 1.1277
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5011 - loss: 1.1277
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5011 - loss: 1.1276
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5011 - loss: 1.1276
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5011 - loss: 1.1276
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5011 - loss: 1.1276
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5011 - loss: 1.1276
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5011 - loss: 1.1275
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5011 - loss: 1.1275
325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5011 - loss: 1.1274
328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5012 - loss: 1.1274
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5012 - loss: 1.1274
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5012 - loss: 1.1273
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5012 - loss: 1.1273
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5012 - loss: 1.1272
342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5012 - loss: 1.1272
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5013 - loss: 1.1272
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5013 - loss: 1.1272
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5013 - loss: 1.1271
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5013 - loss: 1.1271
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5013 - loss: 1.1271
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5013 - loss: 1.1270
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5014 - loss: 1.1270
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5014 - loss: 1.1270
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5014 - loss: 1.1269
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5014 - loss: 1.1269
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5015 - loss: 1.1269
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5015 - loss: 1.1268
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5015 - loss: 1.1268
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5016 - loss: 1.1268
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5016 - loss: 1.1268
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5016 - loss: 1.1267
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5017 - loss: 1.1267
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5017 - loss: 1.1266
397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5017 - loss: 1.1266
400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5018 - loss: 1.1265
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5018 - loss: 1.1264
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5019 - loss: 1.1264
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5019 - loss: 1.1263
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5020 - loss: 1.1263
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5020 - loss: 1.1262
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5020 - loss: 1.1262
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5021 - loss: 1.1261
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5021 - loss: 1.1260
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5021 - loss: 1.1260
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5022 - loss: 1.1259
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5022 - loss: 1.1259
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5022 - loss: 1.1259
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5023 - loss: 1.1258
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5023 - loss: 1.1258
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5023 - loss: 1.1257
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5024 - loss: 1.1257
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5024 - loss: 1.1256
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5024 - loss: 1.1256
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5025 - loss: 1.1255
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5025 - loss: 1.1255
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5025 - loss: 1.1254
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5026 - loss: 1.1254
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5026 - loss: 1.1253
472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5027 - loss: 1.1252
Epoch 5: val_accuracy did not improve from 0.55375
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5027 - loss: 1.1252 - val_accuracy: 0.5409 - val_loss: 1.0454 - learning_rate: 0.0010
Epoch 6/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:49 232ms/step - accuracy: 0.3750 - loss: 1.3090
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.4753 - loss: 1.2070
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5107 - loss: 1.1654
9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5251 - loss: 1.1468
12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5392 - loss: 1.1267
15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5424 - loss: 1.1171
18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5440 - loss: 1.1111
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5449 - loss: 1.1075
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5446 - loss: 1.1045
26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5440 - loss: 1.1037
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5431 - loss: 1.1023
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5420 - loss: 1.1012
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5411 - loss: 1.1003
37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5404 - loss: 1.0998
39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5399 - loss: 1.0992
42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5393 - loss: 1.0981
45/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5388 - loss: 1.0974
48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5383 - loss: 1.0970
51/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5380 - loss: 1.0965
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5375 - loss: 1.0960
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5371 - loss: 1.0956
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5367 - loss: 1.0951
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5364 - loss: 1.0946
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5359 - loss: 1.0942
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5354 - loss: 1.0939
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5348 - loss: 1.0937
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Epoch 6: val_accuracy improved from 0.55375 to 0.55917, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5196 - loss: 1.0945 - val_accuracy: 0.5592 - val_loss: 1.0174 - learning_rate: 0.0010
Epoch 7/40
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309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5191 - loss: 1.0860
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0861
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0862
317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0862
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0863
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0864
325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0864
328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0865
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5192 - loss: 1.0866
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5192 - loss: 1.0867
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0868
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0868
342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0869
345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0870
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0870
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0871
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0872
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0873
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0873
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0874
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0875
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0875
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5194 - loss: 1.0876
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5194 - loss: 1.0876
376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5194 - loss: 1.0877
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5194 - loss: 1.0877
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5194 - loss: 1.0878
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5194 - loss: 1.0878
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5194 - loss: 1.0879
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0879
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0880
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0880
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0881
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0881
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0882
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0882
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0883
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0883
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0884
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0884
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0885
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0885
425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0886
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0886
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0887
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0887
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0888
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0888
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0889
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0889
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0890
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0891
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0891
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0892
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0892
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0892
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0893
Epoch 7: val_accuracy did not improve from 0.55917
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5196 - loss: 1.0894 - val_accuracy: 0.5535 - val_loss: 1.0247 - learning_rate: 0.0010
Epoch 8/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 161ms/step - accuracy: 0.6250 - loss: 0.9302
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6309 - loss: 0.9532
7/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6083 - loss: 0.9931
9/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5945 - loss: 1.0163
11/473 ━━━━━━━━━━━━━━━━━━━━ 12s 27ms/step - accuracy: 0.5836 - loss: 1.0342
14/473 ━━━━━━━━━━━━━━━━━━━━ 11s 26ms/step - accuracy: 0.5713 - loss: 1.0511
17/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.5610 - loss: 1.0636
20/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5546 - loss: 1.0714
23/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5504 - loss: 1.0760
26/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5476 - loss: 1.0793
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5451 - loss: 1.0816
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5430 - loss: 1.0834
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5411 - loss: 1.0855
38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5395 - loss: 1.0878
41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5384 - loss: 1.0893
44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5372 - loss: 1.0906
46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5364 - loss: 1.0915
49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5355 - loss: 1.0927
52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5347 - loss: 1.0938
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5342 - loss: 1.0944
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5336 - loss: 1.0952
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5332 - loss: 1.0957
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5330 - loss: 1.0958
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5331 - loss: 1.0957
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5330 - loss: 1.0956
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5328 - loss: 1.0958
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5325 - loss: 1.0962
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5322 - loss: 1.0967
82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5319 - loss: 1.0970
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5316 - loss: 1.0973
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5314 - loss: 1.0976
90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5312 - loss: 1.0978
93/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5309 - loss: 1.0981
96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5307 - loss: 1.0983
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5306 - loss: 1.0984
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5305 - loss: 1.0985
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5305 - loss: 1.0985
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5304 - loss: 1.0984
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5304 - loss: 1.0984
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5303 - loss: 1.0983
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5303 - loss: 1.0982
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5303 - loss: 1.0981
119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5302 - loss: 1.0980
122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5302 - loss: 1.0980
125/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5302 - loss: 1.0980
128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5302 - loss: 1.0979
131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5302 - loss: 1.0978
134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5302 - loss: 1.0977
137/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5303 - loss: 1.0976
140/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5303 - loss: 1.0976
143/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5304 - loss: 1.0975
146/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5304 - loss: 1.0974
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5305 - loss: 1.0973
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5305 - loss: 1.0973
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5306 - loss: 1.0972
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5306 - loss: 1.0972
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5306 - loss: 1.0972
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5306 - loss: 1.0971
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5306 - loss: 1.0971
170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5306 - loss: 1.0971
173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5306 - loss: 1.0971
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5305 - loss: 1.0972
178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5305 - loss: 1.0972
181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5305 - loss: 1.0972
184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5305 - loss: 1.0972
187/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5305 - loss: 1.0972
190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5305 - loss: 1.0972
193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5305 - loss: 1.0972
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5304 - loss: 1.0972
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5304 - loss: 1.0972
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217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5301 - loss: 1.0973
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223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5300 - loss: 1.0974
226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5299 - loss: 1.0974
229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5299 - loss: 1.0974
232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5298 - loss: 1.0974
235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5297 - loss: 1.0975
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5297 - loss: 1.0975
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5296 - loss: 1.0975
244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5296 - loss: 1.0975
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5295 - loss: 1.0975
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5295 - loss: 1.0975
251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5294 - loss: 1.0975
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5294 - loss: 1.0975
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5293 - loss: 1.0975
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263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5292 - loss: 1.0975
266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5292 - loss: 1.0975
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5292 - loss: 1.0975
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5291 - loss: 1.0975
275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5291 - loss: 1.0975
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5290 - loss: 1.0975
281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5290 - loss: 1.0975
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5289 - loss: 1.0976
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5289 - loss: 1.0976
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5288 - loss: 1.0976
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5288 - loss: 1.0976
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5288 - loss: 1.0976
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5287 - loss: 1.0976
302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5287 - loss: 1.0976
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5287 - loss: 1.0976
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5286 - loss: 1.0976
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5286 - loss: 1.0976
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5285 - loss: 1.0976
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319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5284 - loss: 1.0976
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5284 - loss: 1.0976
325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5284 - loss: 1.0976
328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5283 - loss: 1.0976
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5283 - loss: 1.0976
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5282 - loss: 1.0976
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5282 - loss: 1.0976
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5282 - loss: 1.0976
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5282 - loss: 1.0976
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5282 - loss: 1.0976
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5281 - loss: 1.0975
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5281 - loss: 1.0975
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5281 - loss: 1.0975
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5281 - loss: 1.0975
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5281 - loss: 1.0974
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5281 - loss: 1.0974
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5281 - loss: 1.0974
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5281 - loss: 1.0973
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5281 - loss: 1.0973
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5281 - loss: 1.0973
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5281 - loss: 1.0972
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5281 - loss: 1.0972
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5281 - loss: 1.0972
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5280 - loss: 1.0972
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0971
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0971
397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0971
400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0970
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0970
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0970
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0969
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0969
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0969
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0968
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0968
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0968
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0967
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0967
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0967
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0966
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0966
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0965
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0965
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0965
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0964
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0964
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0963
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0963
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0962
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0962
472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0961
Epoch 8: val_accuracy did not improve from 0.55917
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5278 - loss: 1.0961 - val_accuracy: 0.5341 - val_loss: 1.0665 - learning_rate: 0.0010
Epoch 9/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.5000 - loss: 1.1318
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5208 - loss: 1.1205
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5170 - loss: 1.1355
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5140 - loss: 1.1395
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5143 - loss: 1.1339
15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5152 - loss: 1.1290
18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5167 - loss: 1.1240
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5172 - loss: 1.1215
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5187 - loss: 1.1177
27/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5197 - loss: 1.1149
30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5208 - loss: 1.1121
32/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5215 - loss: 1.1102
35/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5221 - loss: 1.1084
38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5227 - loss: 1.1072
41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5231 - loss: 1.1064
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5234 - loss: 1.1057
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5237 - loss: 1.1051
50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5238 - loss: 1.1047
53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5240 - loss: 1.1043
56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5241 - loss: 1.1038
59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5243 - loss: 1.1033
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5244 - loss: 1.1029
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5245 - loss: 1.1023
68/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5247 - loss: 1.1018
71/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5248 - loss: 1.1013
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424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5293 - loss: 1.0852
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5293 - loss: 1.0852
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5292 - loss: 1.0853
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5292 - loss: 1.0853
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5292 - loss: 1.0853
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5291 - loss: 1.0853
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5291 - loss: 1.0854
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5291 - loss: 1.0854
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5290 - loss: 1.0854
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5290 - loss: 1.0854
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5290 - loss: 1.0854
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5289 - loss: 1.0855
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5289 - loss: 1.0855
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5289 - loss: 1.0855
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5288 - loss: 1.0855
472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5288 - loss: 1.0856
Epoch 9: val_accuracy did not improve from 0.55917
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5288 - loss: 1.0856 - val_accuracy: 0.5357 - val_loss: 1.0625 - learning_rate: 0.0010
Epoch 10/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:21 173ms/step - accuracy: 0.4375 - loss: 1.2522
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4635 - loss: 1.1908
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4919 - loss: 1.1545
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5098 - loss: 1.1335
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5185 - loss: 1.1224
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5253 - loss: 1.1132
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5301 - loss: 1.1058
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5329 - loss: 1.1007
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5342 - loss: 1.0979
26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5355 - loss: 1.0957
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5369 - loss: 1.0932
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5380 - loss: 1.0908
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5387 - loss: 1.0890
38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5390 - loss: 1.0873
41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5391 - loss: 1.0858
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5389 - loss: 1.0850
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5388 - loss: 1.0846
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5388 - loss: 1.0839
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5389 - loss: 1.0835
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5391 - loss: 1.0831
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5392 - loss: 1.0826
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5393 - loss: 1.0821
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5393 - loss: 1.0818
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5396 - loss: 1.0814
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5399 - loss: 1.0809
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5402 - loss: 1.0805
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5404 - loss: 1.0802
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5406 - loss: 1.0800
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5407 - loss: 1.0799
84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5408 - loss: 1.0798
86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5409 - loss: 1.0797
89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5411 - loss: 1.0797
92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5413 - loss: 1.0797
95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5415 - loss: 1.0796
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5418 - loss: 1.0794
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5420 - loss: 1.0793
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5423 - loss: 1.0791
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5425 - loss: 1.0790
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5428 - loss: 1.0787
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5431 - loss: 1.0785
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5433 - loss: 1.0782
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5435 - loss: 1.0781
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5436 - loss: 1.0779
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5438 - loss: 1.0778
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5440 - loss: 1.0776
128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5441 - loss: 1.0775
130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5442 - loss: 1.0773
133/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5444 - loss: 1.0771
136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5445 - loss: 1.0769
139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5446 - loss: 1.0766
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5447 - loss: 1.0765
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5447 - loss: 1.0763
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5448 - loss: 1.0760
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5449 - loss: 1.0759
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5449 - loss: 1.0757
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5449 - loss: 1.0755
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5450 - loss: 1.0753
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5450 - loss: 1.0752
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5450 - loss: 1.0751
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5451 - loss: 1.0749
170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5451 - loss: 1.0748
173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5451 - loss: 1.0747
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5451 - loss: 1.0746
178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5451 - loss: 1.0745
181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5451 - loss: 1.0745
184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5450 - loss: 1.0744
187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5450 - loss: 1.0743
190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5450 - loss: 1.0743
193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5449 - loss: 1.0742
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5449 - loss: 1.0742
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5448 - loss: 1.0741
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5448 - loss: 1.0741
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5447 - loss: 1.0740
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5447 - loss: 1.0740
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5446 - loss: 1.0739
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5446 - loss: 1.0739
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5446 - loss: 1.0739
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5445 - loss: 1.0738
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5445 - loss: 1.0738
226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5445 - loss: 1.0738
229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5444 - loss: 1.0737
232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0737
235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0736
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0735
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0735
244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0734
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0733
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0733
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0732
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0732
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0731
260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5443 - loss: 1.0730
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5443 - loss: 1.0729
266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5443 - loss: 1.0729
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5442 - loss: 1.0728
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5442 - loss: 1.0728
275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5441 - loss: 1.0728
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5441 - loss: 1.0727
280/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5440 - loss: 1.0727
283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5440 - loss: 1.0727
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5439 - loss: 1.0726
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5439 - loss: 1.0726
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5438 - loss: 1.0726
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5438 - loss: 1.0726
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5437 - loss: 1.0726
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5437 - loss: 1.0725
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5436 - loss: 1.0725
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5436 - loss: 1.0725
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5435 - loss: 1.0725
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5434 - loss: 1.0725
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5434 - loss: 1.0725
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5433 - loss: 1.0725
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5433 - loss: 1.0725
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5432 - loss: 1.0725
325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5432 - loss: 1.0725
328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5431 - loss: 1.0725
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5430 - loss: 1.0725
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5430 - loss: 1.0726
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5429 - loss: 1.0726
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5429 - loss: 1.0726
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5428 - loss: 1.0726
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5428 - loss: 1.0726
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5427 - loss: 1.0726
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5427 - loss: 1.0726
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5427 - loss: 1.0726
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5426 - loss: 1.0726
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5426 - loss: 1.0726
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5426 - loss: 1.0726
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5425 - loss: 1.0726
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5425 - loss: 1.0727
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5425 - loss: 1.0727
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5424 - loss: 1.0727
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5424 - loss: 1.0727
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5423 - loss: 1.0727
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5423 - loss: 1.0727
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5423 - loss: 1.0727
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5422 - loss: 1.0728
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5422 - loss: 1.0728
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5421 - loss: 1.0728
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5421 - loss: 1.0728
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5421 - loss: 1.0728
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5420 - loss: 1.0729
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5420 - loss: 1.0729
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5420 - loss: 1.0729
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5419 - loss: 1.0729
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5419 - loss: 1.0729
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5418 - loss: 1.0730
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5418 - loss: 1.0730
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5418 - loss: 1.0730
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5417 - loss: 1.0730
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5417 - loss: 1.0730
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5416 - loss: 1.0731
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5416 - loss: 1.0731
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5415 - loss: 1.0731
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5415 - loss: 1.0731
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5415 - loss: 1.0732
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5414 - loss: 1.0732
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5414 - loss: 1.0732
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5413 - loss: 1.0732
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5413 - loss: 1.0732
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5413 - loss: 1.0733
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5412 - loss: 1.0733
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5412 - loss: 1.0733
473/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5410 - loss: 1.0734
Epoch 10: val_accuracy did not improve from 0.55917
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5410 - loss: 1.0734 - val_accuracy: 0.5166 - val_loss: 1.0749 - learning_rate: 0.0010
Epoch 11/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:28 187ms/step - accuracy: 0.5312 - loss: 1.0139
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4980 - loss: 1.0463
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5017 - loss: 1.0491
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5061 - loss: 1.0504
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5109 - loss: 1.0500
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5149 - loss: 1.0492
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5175 - loss: 1.0502
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5192 - loss: 1.0501
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5219 - loss: 1.0502
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5234 - loss: 1.0507
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5250 - loss: 1.0510
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5265 - loss: 1.0511
36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5277 - loss: 1.0514
39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5288 - loss: 1.0520
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5299 - loss: 1.0527
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5307 - loss: 1.0533
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5314 - loss: 1.0543
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5322 - loss: 1.0551
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5328 - loss: 1.0559
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5334 - loss: 1.0566
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5339 - loss: 1.0572
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5344 - loss: 1.0575
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5347 - loss: 1.0578
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5351 - loss: 1.0582
71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5353 - loss: 1.0584
74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5354 - loss: 1.0588
77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5355 - loss: 1.0594
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5356 - loss: 1.0598
82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5356 - loss: 1.0605
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5355 - loss: 1.0612
88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5353 - loss: 1.0619
91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5351 - loss: 1.0626
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5350 - loss: 1.0632
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5349 - loss: 1.0637
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5347 - loss: 1.0643
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5345 - loss: 1.0649
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5344 - loss: 1.0654
108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5343 - loss: 1.0659
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5342 - loss: 1.0663
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5342 - loss: 1.0666
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5341 - loss: 1.0670
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5341 - loss: 1.0673
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5341 - loss: 1.0676
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5341 - loss: 1.0679
129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5341 - loss: 1.0681
132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5341 - loss: 1.0683
135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5341 - loss: 1.0685
138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5341 - loss: 1.0687
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5341 - loss: 1.0689
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5341 - loss: 1.0690
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5342 - loss: 1.0692
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5342 - loss: 1.0694
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5341 - loss: 1.0695
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5341 - loss: 1.0697
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5340 - loss: 1.0699
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5339 - loss: 1.0702
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5338 - loss: 1.0705
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5337 - loss: 1.0708
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5336 - loss: 1.0710
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5335 - loss: 1.0712
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5334 - loss: 1.0714
179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5334 - loss: 1.0716
182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5333 - loss: 1.0718
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5333 - loss: 1.0719
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5332 - loss: 1.0721
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5332 - loss: 1.0723
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5331 - loss: 1.0725
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5330 - loss: 1.0727
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203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5329 - loss: 1.0732
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209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5327 - loss: 1.0736
212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5327 - loss: 1.0738
215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5326 - loss: 1.0740
218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5325 - loss: 1.0742
221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5324 - loss: 1.0744
224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5323 - loss: 1.0746
227/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5322 - loss: 1.0748
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5321 - loss: 1.0750
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5320 - loss: 1.0752
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5319 - loss: 1.0754
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5318 - loss: 1.0756
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5317 - loss: 1.0758
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5316 - loss: 1.0760
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5315 - loss: 1.0762
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5314 - loss: 1.0763
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5313 - loss: 1.0765
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5313 - loss: 1.0767
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5312 - loss: 1.0769
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5311 - loss: 1.0770
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5311 - loss: 1.0772
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5310 - loss: 1.0773
273/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5309 - loss: 1.0775
276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5309 - loss: 1.0776
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5308 - loss: 1.0777
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5308 - loss: 1.0778
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5307 - loss: 1.0779
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5307 - loss: 1.0781
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5306 - loss: 1.0782
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5306 - loss: 1.0783
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5305 - loss: 1.0784
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5305 - loss: 1.0785
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5304 - loss: 1.0786
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5304 - loss: 1.0787
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5304 - loss: 1.0788
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5303 - loss: 1.0789
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5303 - loss: 1.0790
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5302 - loss: 1.0790
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5302 - loss: 1.0791
324/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5301 - loss: 1.0792
327/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5301 - loss: 1.0793
330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5301 - loss: 1.0794
333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5300 - loss: 1.0794
336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5300 - loss: 1.0795
339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5300 - loss: 1.0796
342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5299 - loss: 1.0796
345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5299 - loss: 1.0797
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5299 - loss: 1.0798
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5298 - loss: 1.0799
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5298 - loss: 1.0799
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5298 - loss: 1.0800
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5298 - loss: 1.0801
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5297 - loss: 1.0801
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5297 - loss: 1.0802
369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5297 - loss: 1.0803
372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5296 - loss: 1.0803
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5296 - loss: 1.0804
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5296 - loss: 1.0804
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5295 - loss: 1.0805
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5295 - loss: 1.0805
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5295 - loss: 1.0806
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5295 - loss: 1.0806
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5295 - loss: 1.0807
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0807
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0807
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0808
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0808
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0808
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0808
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0809
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0809
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0809
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5293 - loss: 1.0810
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5293 - loss: 1.0810
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5293 - loss: 1.0810
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5293 - loss: 1.0811
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5293 - loss: 1.0811
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0811
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0812
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0812
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0812
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0812
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0812
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0813
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0813
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0813
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5291 - loss: 1.0813
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5291 - loss: 1.0813
472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5291 - loss: 1.0814
Epoch 11: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.
Epoch 11: val_accuracy did not improve from 0.55917
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5291 - loss: 1.0814 - val_accuracy: 0.5477 - val_loss: 1.0631 - learning_rate: 0.0010
Epoch 12/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 160ms/step - accuracy: 0.6562 - loss: 1.0261
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5788 - loss: 1.0697
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5593 - loss: 1.0767
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5515 - loss: 1.0740
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5498 - loss: 1.0712
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5503 - loss: 1.0649
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5500 - loss: 1.0636
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5496 - loss: 1.0640
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5490 - loss: 1.0657
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5481 - loss: 1.0673
30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5481 - loss: 1.0681
33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5480 - loss: 1.0680
36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5477 - loss: 1.0679
39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5475 - loss: 1.0674
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5475 - loss: 1.0669
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5478 - loss: 1.0659
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5480 - loss: 1.0653
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5479 - loss: 1.0649
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5476 - loss: 1.0647
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5474 - loss: 1.0645
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5471 - loss: 1.0645
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5469 - loss: 1.0644
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5469 - loss: 1.0641
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5468 - loss: 1.0639
72/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5468 - loss: 1.0635
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Epoch 12: val_accuracy improved from 0.55917 to 0.57062, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5510 - loss: 1.0490 - val_accuracy: 0.5706 - val_loss: 1.0130 - learning_rate: 2.0000e-04
Epoch 13/40
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7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.4732 - loss: 1.1334
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Epoch 13: val_accuracy improved from 0.57062 to 0.57304, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5456 - loss: 1.0510 - val_accuracy: 0.5730 - val_loss: 0.9909 - learning_rate: 2.0000e-04
Epoch 14/40
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211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5482 - loss: 1.0488
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5484 - loss: 1.0486
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5485 - loss: 1.0483
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5486 - loss: 1.0481
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5487 - loss: 1.0479
226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5488 - loss: 1.0477
229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5489 - loss: 1.0475
232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5490 - loss: 1.0472
235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5491 - loss: 1.0470
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5493 - loss: 1.0468
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5494 - loss: 1.0466
244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5495 - loss: 1.0464
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5496 - loss: 1.0462
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5497 - loss: 1.0460
253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5499 - loss: 1.0458
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5500 - loss: 1.0456
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5501 - loss: 1.0454
262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5502 - loss: 1.0452
265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5504 - loss: 1.0451
268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5505 - loss: 1.0449
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5506 - loss: 1.0448
274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5507 - loss: 1.0446
277/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5508 - loss: 1.0445
280/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5509 - loss: 1.0444
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5510 - loss: 1.0443
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5510 - loss: 1.0442
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5511 - loss: 1.0441
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5512 - loss: 1.0440
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5513 - loss: 1.0439
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5514 - loss: 1.0438
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5515 - loss: 1.0437
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5515 - loss: 1.0436
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5516 - loss: 1.0435
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5517 - loss: 1.0434
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5518 - loss: 1.0433
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5518 - loss: 1.0432
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5519 - loss: 1.0431
317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5520 - loss: 1.0431
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5520 - loss: 1.0430
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5521 - loss: 1.0429
325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5522 - loss: 1.0428
328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5522 - loss: 1.0427
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5523 - loss: 1.0426
333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5524 - loss: 1.0426
336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5524 - loss: 1.0425
339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5525 - loss: 1.0424
342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5526 - loss: 1.0423
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5526 - loss: 1.0422
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5527 - loss: 1.0422
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5527 - loss: 1.0421
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5528 - loss: 1.0420
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5529 - loss: 1.0419
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5530 - loss: 1.0418
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5530 - loss: 1.0417
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5531 - loss: 1.0416
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5532 - loss: 1.0415
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5533 - loss: 1.0414
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5533 - loss: 1.0413
376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5534 - loss: 1.0412
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5535 - loss: 1.0411
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5535 - loss: 1.0411
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5536 - loss: 1.0410
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5536 - loss: 1.0410
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5537 - loss: 1.0409
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5537 - loss: 1.0408
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5537 - loss: 1.0408
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5538 - loss: 1.0407
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5538 - loss: 1.0407
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5539 - loss: 1.0406
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5539 - loss: 1.0406
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5540 - loss: 1.0405
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5540 - loss: 1.0405
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5540 - loss: 1.0404
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5541 - loss: 1.0404
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5541 - loss: 1.0403
425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5542 - loss: 1.0403
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5542 - loss: 1.0402
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5543 - loss: 1.0402
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5543 - loss: 1.0401
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5543 - loss: 1.0401
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5544 - loss: 1.0400
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5544 - loss: 1.0400
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5544 - loss: 1.0399
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5545 - loss: 1.0399
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5545 - loss: 1.0399
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5545 - loss: 1.0398
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0398
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0398
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0397
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0397
Epoch 14: val_accuracy did not improve from 0.57304
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5547 - loss: 1.0396 - val_accuracy: 0.5640 - val_loss: 1.0161 - learning_rate: 2.0000e-04
Epoch 15/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 161ms/step - accuracy: 0.5000 - loss: 1.1099
4/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5319 - loss: 1.0903
7/473 ━━━━━━━━━━━━━━━━━━━━ 12s 26ms/step - accuracy: 0.5346 - loss: 1.0911
10/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5363 - loss: 1.0867
13/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5359 - loss: 1.0808
16/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5349 - loss: 1.0785
19/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5349 - loss: 1.0752
22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5351 - loss: 1.0725
25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5355 - loss: 1.0700
28/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5361 - loss: 1.0683
31/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5366 - loss: 1.0664
34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5374 - loss: 1.0643
37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5383 - loss: 1.0620
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5391 - loss: 1.0599
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5398 - loss: 1.0580
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5407 - loss: 1.0563
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5416 - loss: 1.0545
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5424 - loss: 1.0531
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5430 - loss: 1.0521
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5434 - loss: 1.0514
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5436 - loss: 1.0508
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5438 - loss: 1.0503
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5439 - loss: 1.0501
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5439 - loss: 1.0498
71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5439 - loss: 1.0496
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5440 - loss: 1.0491
78/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5440 - loss: 1.0489
81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5440 - loss: 1.0487
84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5441 - loss: 1.0484
87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5443 - loss: 1.0480
90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5446 - loss: 1.0476
93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5448 - loss: 1.0472
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445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5494 - loss: 1.0412
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451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5494 - loss: 1.0412
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5494 - loss: 1.0412
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5494 - loss: 1.0412
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5494 - loss: 1.0411
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5495 - loss: 1.0411
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5495 - loss: 1.0411
Epoch 15: val_accuracy did not improve from 0.57304
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5495 - loss: 1.0410 - val_accuracy: 0.5674 - val_loss: 1.0008 - learning_rate: 2.0000e-04
Epoch 16/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:20 171ms/step - accuracy: 0.5000 - loss: 1.0865
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5651 - loss: 1.0210
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5681 - loss: 1.0212
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5650 - loss: 1.0254
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5646 - loss: 1.0254
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5648 - loss: 1.0246
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5660 - loss: 1.0235
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5674 - loss: 1.0219
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5678 - loss: 1.0202
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5677 - loss: 1.0188
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5673 - loss: 1.0177
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5668 - loss: 1.0166
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5663 - loss: 1.0160
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5658 - loss: 1.0159
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5649 - loss: 1.0166
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5643 - loss: 1.0170
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5637 - loss: 1.0176
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5633 - loss: 1.0180
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5630 - loss: 1.0186
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5629 - loss: 1.0188
59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5630 - loss: 1.0189
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5630 - loss: 1.0189
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5632 - loss: 1.0188
68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5633 - loss: 1.0186
71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5635 - loss: 1.0185
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5636 - loss: 1.0183
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5637 - loss: 1.0182
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5638 - loss: 1.0182
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5640 - loss: 1.0181
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5642 - loss: 1.0179
87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5643 - loss: 1.0179
90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5645 - loss: 1.0177
92/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5646 - loss: 1.0176
95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5648 - loss: 1.0174
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5650 - loss: 1.0172
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5652 - loss: 1.0170
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5655 - loss: 1.0168
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5656 - loss: 1.0167
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5658 - loss: 1.0166
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5660 - loss: 1.0165
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5661 - loss: 1.0165
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5662 - loss: 1.0164
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5664 - loss: 1.0164
122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5664 - loss: 1.0164
125/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5665 - loss: 1.0164
128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5666 - loss: 1.0164
131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5666 - loss: 1.0164
134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5667 - loss: 1.0165
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5667 - loss: 1.0165
140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5667 - loss: 1.0165
143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5667 - loss: 1.0166
145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5667 - loss: 1.0166
148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5666 - loss: 1.0167
151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5666 - loss: 1.0168
154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5665 - loss: 1.0169
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5665 - loss: 1.0171
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5664 - loss: 1.0172
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5663 - loss: 1.0174
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5662 - loss: 1.0176
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5660 - loss: 1.0179
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5659 - loss: 1.0182
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5658 - loss: 1.0183
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5657 - loss: 1.0185
179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5656 - loss: 1.0187
182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5655 - loss: 1.0190
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5654 - loss: 1.0192
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5653 - loss: 1.0195
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5652 - loss: 1.0197
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5651 - loss: 1.0199
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5650 - loss: 1.0201
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5649 - loss: 1.0203
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5648 - loss: 1.0204
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5647 - loss: 1.0206
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5646 - loss: 1.0207
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5645 - loss: 1.0209
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5644 - loss: 1.0210
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5643 - loss: 1.0211
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5643 - loss: 1.0212
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5642 - loss: 1.0214
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5641 - loss: 1.0215
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5639 - loss: 1.0217
231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5639 - loss: 1.0218
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5638 - loss: 1.0220
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5637 - loss: 1.0221
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5636 - loss: 1.0222
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5636 - loss: 1.0223
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5635 - loss: 1.0224
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5635 - loss: 1.0225
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5634 - loss: 1.0226
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5633 - loss: 1.0227
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5633 - loss: 1.0228
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5632 - loss: 1.0229
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5631 - loss: 1.0230
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5631 - loss: 1.0231
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5630 - loss: 1.0231
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5630 - loss: 1.0232
275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5629 - loss: 1.0233
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5629 - loss: 1.0234
281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5628 - loss: 1.0234
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5628 - loss: 1.0235
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5627 - loss: 1.0236
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5627 - loss: 1.0236
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5626 - loss: 1.0237
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5626 - loss: 1.0238
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5625 - loss: 1.0238
302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5625 - loss: 1.0239
305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5625 - loss: 1.0239
308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5624 - loss: 1.0240
311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5624 - loss: 1.0240
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5624 - loss: 1.0240
317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5623 - loss: 1.0241
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5623 - loss: 1.0241
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5623 - loss: 1.0241
326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5622 - loss: 1.0242
329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5622 - loss: 1.0242
332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5622 - loss: 1.0242
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5622 - loss: 1.0242
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5622 - loss: 1.0243
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0243
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0243
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0243
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0243
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0243
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0243
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0244
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5620 - loss: 1.0244
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5620 - loss: 1.0244
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5620 - loss: 1.0244
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5620 - loss: 1.0244
374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5620 - loss: 1.0245
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5620 - loss: 1.0245
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0245
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0245
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0245
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0246
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0246
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0246
397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0246
400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0246
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0246
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0246
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0246
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0247
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0247
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0247
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5617 - loss: 1.0247
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5617 - loss: 1.0247
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5617 - loss: 1.0247
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5617 - loss: 1.0247
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5616 - loss: 1.0247
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5616 - loss: 1.0248
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5616 - loss: 1.0248
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5616 - loss: 1.0248
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5616 - loss: 1.0248
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5616 - loss: 1.0248
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5615 - loss: 1.0248
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5615 - loss: 1.0249
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5615 - loss: 1.0249
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5614 - loss: 1.0249
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5614 - loss: 1.0250
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5614 - loss: 1.0250
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5613 - loss: 1.0251
472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5612 - loss: 1.0252
Epoch 16: val_accuracy did not improve from 0.57304
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5612 - loss: 1.0252 - val_accuracy: 0.5322 - val_loss: 1.0739 - learning_rate: 2.0000e-04
Epoch 17/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 163ms/step - accuracy: 0.4688 - loss: 1.1418
3/473 ━━━━━━━━━━━━━━━━━━━━ 16s 35ms/step - accuracy: 0.5087 - loss: 1.0576
6/473 ━━━━━━━━━━━━━━━━━━━━ 12s 28ms/step - accuracy: 0.5242 - loss: 1.0388
9/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.5362 - loss: 1.0323
12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5429 - loss: 1.0319
15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5483 - loss: 1.0293
18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5535 - loss: 1.0253
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5568 - loss: 1.0213
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5583 - loss: 1.0193
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5598 - loss: 1.0178
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5611 - loss: 1.0175
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5619 - loss: 1.0177
36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5619 - loss: 1.0191
39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5616 - loss: 1.0207
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5609 - loss: 1.0224
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5606 - loss: 1.0235
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5605 - loss: 1.0242
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5603 - loss: 1.0249
53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5602 - loss: 1.0253
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5600 - loss: 1.0258
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5598 - loss: 1.0264
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5598 - loss: 1.0269
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5596 - loss: 1.0275
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5595 - loss: 1.0280
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5595 - loss: 1.0284
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5595 - loss: 1.0285
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5596 - loss: 1.0285
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5597 - loss: 1.0284
82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5597 - loss: 1.0284
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5598 - loss: 1.0284
88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5598 - loss: 1.0285
91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5597 - loss: 1.0286
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5597 - loss: 1.0287
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5596 - loss: 1.0288
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5596 - loss: 1.0288
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5595 - loss: 1.0289
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5595 - loss: 1.0290
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5595 - loss: 1.0290
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5594 - loss: 1.0290
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5593 - loss: 1.0291
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5591 - loss: 1.0293
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5590 - loss: 1.0295
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5588 - loss: 1.0297
127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5588 - loss: 1.0298
130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5587 - loss: 1.0299
133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5586 - loss: 1.0300
136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5586 - loss: 1.0300
139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5586 - loss: 1.0301
142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5586 - loss: 1.0301
145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5585 - loss: 1.0302
148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5585 - loss: 1.0302
151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5584 - loss: 1.0303
154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5584 - loss: 1.0304
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5583 - loss: 1.0305
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5582 - loss: 1.0305
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5581 - loss: 1.0306
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5581 - loss: 1.0307
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5580 - loss: 1.0307
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5580 - loss: 1.0308
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5580 - loss: 1.0308
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5579 - loss: 1.0308
179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5579 - loss: 1.0309
182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5579 - loss: 1.0309
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5579 - loss: 1.0310
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5579 - loss: 1.0310
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5579 - loss: 1.0311
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5579 - loss: 1.0311
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5580 - loss: 1.0312
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5580 - loss: 1.0312
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5580 - loss: 1.0312
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5580 - loss: 1.0312
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5580 - loss: 1.0313
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5580 - loss: 1.0313
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5581 - loss: 1.0313
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5581 - loss: 1.0314
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5581 - loss: 1.0314
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5581 - loss: 1.0314
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5581 - loss: 1.0315
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5581 - loss: 1.0315
231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5581 - loss: 1.0315
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5581 - loss: 1.0316
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5581 - loss: 1.0316
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5581 - loss: 1.0316
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5581 - loss: 1.0316
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5581 - loss: 1.0316
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5582 - loss: 1.0316
251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5582 - loss: 1.0316
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5582 - loss: 1.0316
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5582 - loss: 1.0316
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5582 - loss: 1.0316
262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5582 - loss: 1.0316
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5582 - loss: 1.0316
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5582 - loss: 1.0316
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5582 - loss: 1.0317
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5582 - loss: 1.0317
275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5581 - loss: 1.0317
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5581 - loss: 1.0317
281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5581 - loss: 1.0317
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5581 - loss: 1.0317
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5581 - loss: 1.0317
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5581 - loss: 1.0318
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5581 - loss: 1.0318
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5581 - loss: 1.0318
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5581 - loss: 1.0318
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5580 - loss: 1.0318
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5580 - loss: 1.0318
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5580 - loss: 1.0318
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5580 - loss: 1.0318
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5580 - loss: 1.0319
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5580 - loss: 1.0319
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5579 - loss: 1.0319
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5579 - loss: 1.0319
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5579 - loss: 1.0319
326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5579 - loss: 1.0319
329/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5579 - loss: 1.0319
332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5579 - loss: 1.0319
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5579 - loss: 1.0319
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5579 - loss: 1.0319
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5578 - loss: 1.0319
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5578 - loss: 1.0319
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5578 - loss: 1.0320
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5578 - loss: 1.0320
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5578 - loss: 1.0320
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5578 - loss: 1.0319
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5578 - loss: 1.0319
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5578 - loss: 1.0319
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5578 - loss: 1.0319
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5577 - loss: 1.0319
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5577 - loss: 1.0319
374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5577 - loss: 1.0320
376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5577 - loss: 1.0320
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5577 - loss: 1.0320
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5577 - loss: 1.0320
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5577 - loss: 1.0320
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5577 - loss: 1.0320
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5576 - loss: 1.0320
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5576 - loss: 1.0320
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5576 - loss: 1.0321
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5576 - loss: 1.0321
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5576 - loss: 1.0321
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5575 - loss: 1.0321
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5575 - loss: 1.0321
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5575 - loss: 1.0322
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5575 - loss: 1.0322
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5575 - loss: 1.0322
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5575 - loss: 1.0322
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5574 - loss: 1.0323
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5574 - loss: 1.0323
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5574 - loss: 1.0323
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5574 - loss: 1.0324
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0324
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0324
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0325
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0325
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0325
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0325
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0326
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0326
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5572 - loss: 1.0326
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5572 - loss: 1.0326
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5572 - loss: 1.0326
Epoch 17: val_accuracy did not improve from 0.57304
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5572 - loss: 1.0327 - val_accuracy: 0.5716 - val_loss: 0.9860 - learning_rate: 2.0000e-04
Epoch 18/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.6562 - loss: 0.8536
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.6204 - loss: 0.9114
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6065 - loss: 0.9348
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5962 - loss: 0.9524
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5882 - loss: 0.9677
17/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5815 - loss: 0.9785
20/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5772 - loss: 0.9870
23/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5732 - loss: 0.9935
26/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5696 - loss: 0.9981
29/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5675 - loss: 1.0017
32/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5661 - loss: 1.0044
35/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5655 - loss: 1.0063
38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5651 - loss: 1.0080
41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5645 - loss: 1.0100
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5641 - loss: 1.0112
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5640 - loss: 1.0119
50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5641 - loss: 1.0122
53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5642 - loss: 1.0125
56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5642 - loss: 1.0128
59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5641 - loss: 1.0130
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5640 - loss: 1.0132
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5639 - loss: 1.0134
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5638 - loss: 1.0137
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5636 - loss: 1.0142
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5633 - loss: 1.0147
75/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0150
78/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0152
81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0154
84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0157
87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5634 - loss: 1.0159
90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5634 - loss: 1.0161
93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5634 - loss: 1.0162
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5634 - loss: 1.0164
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5634 - loss: 1.0166
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0168
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0170
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Epoch 18: val_accuracy improved from 0.57304 to 0.57746, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5604 - loss: 1.0255 - val_accuracy: 0.5775 - val_loss: 0.9880 - learning_rate: 2.0000e-04
Epoch 19/40
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3/473 ━━━━━━━━━━━━━━━━━━━━ 12s 26ms/step - accuracy: 0.4809 - loss: 1.2007
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Epoch 19: val_accuracy improved from 0.57746 to 0.58188, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5605 - loss: 1.0270 - val_accuracy: 0.5819 - val_loss: 0.9830 - learning_rate: 2.0000e-04
Epoch 20/40
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Epoch 20: val_accuracy improved from 0.58188 to 0.58268, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5587 - loss: 1.0308 - val_accuracy: 0.5827 - val_loss: 0.9844 - learning_rate: 2.0000e-04
Epoch 21/40
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457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0207
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0207
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5683 - loss: 1.0207
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5683 - loss: 1.0208
Epoch 21: val_accuracy did not improve from 0.58268
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5682 - loss: 1.0208 - val_accuracy: 0.5799 - val_loss: 0.9848 - learning_rate: 2.0000e-04
Epoch 22/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 163ms/step - accuracy: 0.5312 - loss: 1.0835
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5703 - loss: 1.0478
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5619 - loss: 1.0701
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5530 - loss: 1.0808
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5516 - loss: 1.0795
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5491 - loss: 1.0765
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5477 - loss: 1.0722
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5471 - loss: 1.0680
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5470 - loss: 1.0640
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5475 - loss: 1.0592
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5485 - loss: 1.0547
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5496 - loss: 1.0506
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5509 - loss: 1.0467
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5522 - loss: 1.0433
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5533 - loss: 1.0409
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5544 - loss: 1.0388
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5550 - loss: 1.0376
50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5555 - loss: 1.0364
53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5565 - loss: 1.0347
56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5573 - loss: 1.0333
59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5582 - loss: 1.0319
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5590 - loss: 1.0307
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5598 - loss: 1.0297
68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5605 - loss: 1.0289
71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5611 - loss: 1.0281
74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5615 - loss: 1.0275
77/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5620 - loss: 1.0269
80/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5624 - loss: 1.0262
83/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5628 - loss: 1.0255
86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5631 - loss: 1.0250
89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0246
92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5635 - loss: 1.0242
95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5636 - loss: 1.0239
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5638 - loss: 1.0236
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5639 - loss: 1.0232
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5639 - loss: 1.0229
108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5640 - loss: 1.0226
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5640 - loss: 1.0224
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5640 - loss: 1.0221
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5639 - loss: 1.0219
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5639 - loss: 1.0217
123/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5638 - loss: 1.0215
126/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5638 - loss: 1.0213
129/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0211
132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0210
135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0209
138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0209
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0208
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0207
146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0207
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0206
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0207
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5638 - loss: 1.0207
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5638 - loss: 1.0206
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5639 - loss: 1.0205
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5640 - loss: 1.0204
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5641 - loss: 1.0203
170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5642 - loss: 1.0202
173/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5643 - loss: 1.0201
176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5643 - loss: 1.0200
179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5644 - loss: 1.0199
182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5644 - loss: 1.0199
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5645 - loss: 1.0199
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5645 - loss: 1.0198
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5645 - loss: 1.0198
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5646 - loss: 1.0197
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5646 - loss: 1.0197
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5646 - loss: 1.0197
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0197
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0197
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0198
212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0198
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0198
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0198
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0198
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0198
226/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5647 - loss: 1.0199
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5647 - loss: 1.0199
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5647 - loss: 1.0200
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5647 - loss: 1.0201
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5647 - loss: 1.0201
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5647 - loss: 1.0202
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5646 - loss: 1.0203
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5646 - loss: 1.0203
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5646 - loss: 1.0204
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5646 - loss: 1.0205
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5646 - loss: 1.0206
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5646 - loss: 1.0207
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5645 - loss: 1.0208
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5645 - loss: 1.0208
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5645 - loss: 1.0209
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5645 - loss: 1.0210
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5644 - loss: 1.0211
276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5644 - loss: 1.0212
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5644 - loss: 1.0212
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5644 - loss: 1.0213
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5644 - loss: 1.0214
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5644 - loss: 1.0215
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0215
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0216
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0217
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0218
302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0218
305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0219
308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0219
311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5642 - loss: 1.0220
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5642 - loss: 1.0221
317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5642 - loss: 1.0221
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5642 - loss: 1.0222
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5642 - loss: 1.0222
326/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5642 - loss: 1.0222
329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5642 - loss: 1.0223
332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5642 - loss: 1.0223
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5642 - loss: 1.0223
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0224
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0224
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0224
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0225
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0225
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0225
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0226
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0226
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0226
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0226
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0226
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5640 - loss: 1.0227
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0227
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0227
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0227
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0228
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0228
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0228
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0228
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0228
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0230
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0230
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0230
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0230
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0230
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0231
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0231
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0231
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0231
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0232
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0232
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0232
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0232
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5637 - loss: 1.0232
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5637 - loss: 1.0233
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5637 - loss: 1.0233
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5637 - loss: 1.0233
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5637 - loss: 1.0233
472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5637 - loss: 1.0234
Epoch 22: val_accuracy did not improve from 0.58268
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5636 - loss: 1.0234 - val_accuracy: 0.5650 - val_loss: 1.0085 - learning_rate: 2.0000e-04
Epoch 23/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 160ms/step - accuracy: 0.6875 - loss: 0.8717
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.6016 - loss: 0.9734
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5868 - loss: 1.0083
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5851 - loss: 1.0162
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5848 - loss: 1.0141
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5866 - loss: 1.0092
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5856 - loss: 1.0094
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5861 - loss: 1.0079
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5861 - loss: 1.0064
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5858 - loss: 1.0052
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5845 - loss: 1.0050
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5831 - loss: 1.0051
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5818 - loss: 1.0051
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5810 - loss: 1.0051
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5807 - loss: 1.0047
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5802 - loss: 1.0048
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5798 - loss: 1.0046
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5794 - loss: 1.0042
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5793 - loss: 1.0036
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5792 - loss: 1.0030
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5792 - loss: 1.0026
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5791 - loss: 1.0024
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5789 - loss: 1.0020
68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5786 - loss: 1.0018
71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5785 - loss: 1.0016
74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5786 - loss: 1.0013
77/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5786 - loss: 1.0010
80/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5786 - loss: 1.0007
82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5786 - loss: 1.0006
85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5785 - loss: 1.0005
88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5785 - loss: 1.0004
91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5784 - loss: 1.0003
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5783 - loss: 1.0004
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5781 - loss: 1.0005
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5780 - loss: 1.0005
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5779 - loss: 1.0007
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5778 - loss: 1.0008
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5776 - loss: 1.0010
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5775 - loss: 1.0013
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5773 - loss: 1.0015
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5771 - loss: 1.0018
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5770 - loss: 1.0019
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5769 - loss: 1.0021
127/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5768 - loss: 1.0022
130/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5767 - loss: 1.0024
133/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5766 - loss: 1.0027
136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5766 - loss: 1.0029
139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5764 - loss: 1.0031
142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5763 - loss: 1.0033
145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5762 - loss: 1.0036
148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5760 - loss: 1.0038
151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5759 - loss: 1.0041
154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5757 - loss: 1.0043
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5756 - loss: 1.0045
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5755 - loss: 1.0047
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5753 - loss: 1.0049
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5752 - loss: 1.0051
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5751 - loss: 1.0053
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5749 - loss: 1.0056
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5747 - loss: 1.0058
175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5746 - loss: 1.0061
178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5744 - loss: 1.0063
181/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5743 - loss: 1.0065
183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5742 - loss: 1.0066
186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5741 - loss: 1.0068
189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5739 - loss: 1.0071
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5738 - loss: 1.0073
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5737 - loss: 1.0075
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5736 - loss: 1.0077
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5735 - loss: 1.0079
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5734 - loss: 1.0081
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5733 - loss: 1.0083
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5731 - loss: 1.0086
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5730 - loss: 1.0087
215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5730 - loss: 1.0089
218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5728 - loss: 1.0091
221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5727 - loss: 1.0093
224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5726 - loss: 1.0094
226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5725 - loss: 1.0096
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5724 - loss: 1.0097
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5723 - loss: 1.0099
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5722 - loss: 1.0101
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5720 - loss: 1.0103
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5719 - loss: 1.0105
242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5718 - loss: 1.0106
245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5717 - loss: 1.0108
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5715 - loss: 1.0110
251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5714 - loss: 1.0112
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5713 - loss: 1.0113
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5712 - loss: 1.0115
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5711 - loss: 1.0117
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5710 - loss: 1.0118
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5709 - loss: 1.0119
265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5709 - loss: 1.0120
268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5708 - loss: 1.0121
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5707 - loss: 1.0123
274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5706 - loss: 1.0124
276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5705 - loss: 1.0125
279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5704 - loss: 1.0127
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5703 - loss: 1.0128
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5702 - loss: 1.0130
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5702 - loss: 1.0131
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5701 - loss: 1.0132
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5700 - loss: 1.0134
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5699 - loss: 1.0135
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5699 - loss: 1.0136
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5698 - loss: 1.0137
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5697 - loss: 1.0139
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5696 - loss: 1.0140
308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5696 - loss: 1.0141
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5695 - loss: 1.0142
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5694 - loss: 1.0143
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5694 - loss: 1.0145
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5693 - loss: 1.0146
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5692 - loss: 1.0147
324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5692 - loss: 1.0148
327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5691 - loss: 1.0149
330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5691 - loss: 1.0150
333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5690 - loss: 1.0151
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5690 - loss: 1.0152
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5690 - loss: 1.0153
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5689 - loss: 1.0154
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5689 - loss: 1.0154
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5688 - loss: 1.0155
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5688 - loss: 1.0156
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5687 - loss: 1.0157
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5687 - loss: 1.0158
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5686 - loss: 1.0159
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5686 - loss: 1.0160
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5685 - loss: 1.0161
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5685 - loss: 1.0161
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5684 - loss: 1.0162
372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5684 - loss: 1.0163
375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5684 - loss: 1.0164
378/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5683 - loss: 1.0165
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5683 - loss: 1.0165
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5683 - loss: 1.0166
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5682 - loss: 1.0167
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5682 - loss: 1.0167
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5682 - loss: 1.0168
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5681 - loss: 1.0169
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5681 - loss: 1.0169
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5681 - loss: 1.0170
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5680 - loss: 1.0170
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5680 - loss: 1.0171
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5680 - loss: 1.0172
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5679 - loss: 1.0172
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5679 - loss: 1.0173
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5679 - loss: 1.0173
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5679 - loss: 1.0174
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5679 - loss: 1.0174
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0175
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0175
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0175
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0176
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0176
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0177
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0177
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0177
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0177
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0178
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0178
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0178
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5677 - loss: 1.0178
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5677 - loss: 1.0178
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5677 - loss: 1.0179
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5677 - loss: 1.0179
Epoch 23: val_accuracy did not improve from 0.58268
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5677 - loss: 1.0179 - val_accuracy: 0.5734 - val_loss: 0.9839 - learning_rate: 2.0000e-04
Epoch 24/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 166ms/step - accuracy: 0.5625 - loss: 0.9864
3/473 ━━━━━━━━━━━━━━━━━━━━ 16s 35ms/step - accuracy: 0.5729 - loss: 0.9790
5/473 ━━━━━━━━━━━━━━━━━━━━ 17s 37ms/step - accuracy: 0.5884 - loss: 0.9422
7/473 ━━━━━━━━━━━━━━━━━━━━ 15s 34ms/step - accuracy: 0.5941 - loss: 0.9322
10/473 ━━━━━━━━━━━━━━━━━━━━ 13s 29ms/step - accuracy: 0.5942 - loss: 0.9308
13/473 ━━━━━━━━━━━━━━━━━━━━ 12s 27ms/step - accuracy: 0.5926 - loss: 0.9332
15/473 ━━━━━━━━━━━━━━━━━━━━ 12s 26ms/step - accuracy: 0.5934 - loss: 0.9320
18/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5929 - loss: 0.9339
21/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5921 - loss: 0.9386
24/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5910 - loss: 0.9423
27/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5901 - loss: 0.9448
30/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5886 - loss: 0.9495
33/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5872 - loss: 0.9549
36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5858 - loss: 0.9597
39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5844 - loss: 0.9644
42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5834 - loss: 0.9678
44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5830 - loss: 0.9696
47/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5825 - loss: 0.9719
50/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5820 - loss: 0.9740
53/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5815 - loss: 0.9759
55/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5812 - loss: 0.9770
57/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5810 - loss: 0.9781
60/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5807 - loss: 0.9795
63/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5804 - loss: 0.9806
66/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5799 - loss: 0.9818
69/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5795 - loss: 0.9830
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5791 - loss: 0.9840
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5788 - loss: 0.9850
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5786 - loss: 0.9856
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5785 - loss: 0.9863
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5783 - loss: 0.9870
87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5781 - loss: 0.9877
90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5779 - loss: 0.9883
93/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5776 - loss: 0.9891
96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5773 - loss: 0.9899
99/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5771 - loss: 0.9906
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5768 - loss: 0.9914
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453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0123
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0123
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0123
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0123
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0124
Epoch 24: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.
Epoch 24: val_accuracy did not improve from 0.58268
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5684 - loss: 1.0124 - val_accuracy: 0.5728 - val_loss: 0.9909 - learning_rate: 2.0000e-04
Epoch 25/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:21 173ms/step - accuracy: 0.6250 - loss: 0.9017
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5990 - loss: 0.9397
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5865 - loss: 0.9619
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5829 - loss: 0.9735
12/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5800 - loss: 0.9795
15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5760 - loss: 0.9853
18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5746 - loss: 0.9871
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5723 - loss: 0.9905
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5701 - loss: 0.9933
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5691 - loss: 0.9948
30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5682 - loss: 0.9967
33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5676 - loss: 0.9984
36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5675 - loss: 0.9990
39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5673 - loss: 0.9997
41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5673 - loss: 0.9999
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5673 - loss: 0.9999
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5672 - loss: 1.0000
50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5672 - loss: 1.0002
53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5672 - loss: 1.0007
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5674 - loss: 1.0008
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5675 - loss: 1.0011
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5676 - loss: 1.0014
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0016
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0021
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0026
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0031
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5678 - loss: 1.0034
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5678 - loss: 1.0039
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0042
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0045
87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5678 - loss: 1.0047
89/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5678 - loss: 1.0048
92/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0052
95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5677 - loss: 1.0055
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5676 - loss: 1.0057
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5676 - loss: 1.0060
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5676 - loss: 1.0062
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5676 - loss: 1.0063
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5677 - loss: 1.0062
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5677 - loss: 1.0062
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5678 - loss: 1.0062
119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5679 - loss: 1.0063
122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5679 - loss: 1.0064
125/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5679 - loss: 1.0065
128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5678 - loss: 1.0067
131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5677 - loss: 1.0069
133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5677 - loss: 1.0071
136/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5676 - loss: 1.0072
139/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5675 - loss: 1.0074
142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5674 - loss: 1.0077
145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5673 - loss: 1.0079
148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5672 - loss: 1.0080
151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5671 - loss: 1.0083
154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5669 - loss: 1.0085
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5667 - loss: 1.0088
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5666 - loss: 1.0090
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5664 - loss: 1.0092
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5663 - loss: 1.0094
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5662 - loss: 1.0096
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5661 - loss: 1.0097
175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5660 - loss: 1.0099
178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5659 - loss: 1.0100
181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5658 - loss: 1.0101
184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5657 - loss: 1.0103
187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5656 - loss: 1.0104
189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5655 - loss: 1.0105
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5655 - loss: 1.0106
193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5654 - loss: 1.0107
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5653 - loss: 1.0108
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5652 - loss: 1.0110
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5652 - loss: 1.0111
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5651 - loss: 1.0113
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5650 - loss: 1.0114
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5649 - loss: 1.0115
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5648 - loss: 1.0116
218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5647 - loss: 1.0117
221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5646 - loss: 1.0118
224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5645 - loss: 1.0120
227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5644 - loss: 1.0120
230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5644 - loss: 1.0121
233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5643 - loss: 1.0122
236/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5642 - loss: 1.0123
239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5641 - loss: 1.0124
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5641 - loss: 1.0124
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5640 - loss: 1.0125
245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5640 - loss: 1.0125
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5639 - loss: 1.0126
251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5639 - loss: 1.0127
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5638 - loss: 1.0128
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5637 - loss: 1.0128
260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5637 - loss: 1.0129
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5636 - loss: 1.0130
266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5635 - loss: 1.0130
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5635 - loss: 1.0131
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5634 - loss: 1.0131
274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5634 - loss: 1.0132
277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5633 - loss: 1.0132
280/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5633 - loss: 1.0133
283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5632 - loss: 1.0133
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5632 - loss: 1.0134
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5631 - loss: 1.0134
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5631 - loss: 1.0135
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5630 - loss: 1.0135
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5630 - loss: 1.0135
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5629 - loss: 1.0136
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5629 - loss: 1.0136
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5628 - loss: 1.0137
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5628 - loss: 1.0137
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5627 - loss: 1.0137
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5627 - loss: 1.0138
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5627 - loss: 1.0138
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5627 - loss: 1.0138
325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5626 - loss: 1.0138
328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5626 - loss: 1.0138
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5626 - loss: 1.0138
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5626 - loss: 1.0138
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5625 - loss: 1.0139
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5625 - loss: 1.0139
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5625 - loss: 1.0139
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5625 - loss: 1.0139
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139
369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0138
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0138
374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0138
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
Epoch 25: val_accuracy did not improve from 0.58268
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5624 - loss: 1.0136 - val_accuracy: 0.5690 - val_loss: 0.9963 - learning_rate: 4.0000e-05
Epoch 26/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:21 172ms/step - accuracy: 0.5312 - loss: 0.9227
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.6022 - loss: 0.9168
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5856 - loss: 0.9609
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5733 - loss: 0.9828
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5675 - loss: 0.9946
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5657 - loss: 1.0002
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5654 - loss: 1.0031
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5664 - loss: 1.0047
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5666 - loss: 1.0072
29/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5675 - loss: 1.0080
32/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5682 - loss: 1.0081
35/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5690 - loss: 1.0077
38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5700 - loss: 1.0070
41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5710 - loss: 1.0063
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5717 - loss: 1.0056
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5720 - loss: 1.0054
50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5724 - loss: 1.0051
53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5728 - loss: 1.0047
56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5732 - loss: 1.0043
59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5737 - loss: 1.0037
62/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5743 - loss: 1.0030
65/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5749 - loss: 1.0023
68/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5753 - loss: 1.0016
71/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5758 - loss: 1.0009
74/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5762 - loss: 1.0003
77/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5766 - loss: 0.9996
80/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5770 - loss: 0.9990
83/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5773 - loss: 0.9984
86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5776 - loss: 0.9978
89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5778 - loss: 0.9974
92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5781 - loss: 0.9968
95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5785 - loss: 0.9963
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5787 - loss: 0.9958
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5790 - loss: 0.9953
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5793 - loss: 0.9948
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5796 - loss: 0.9943
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5799 - loss: 0.9938
113/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5802 - loss: 0.9933
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5804 - loss: 0.9930
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5805 - loss: 0.9927
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5808 - loss: 0.9924
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5810 - loss: 0.9921
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5812 - loss: 0.9917
129/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5813 - loss: 0.9915
132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5815 - loss: 0.9912
135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5816 - loss: 0.9910
138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5817 - loss: 0.9908
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5818 - loss: 0.9907
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5818 - loss: 0.9905
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5819 - loss: 0.9904
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5819 - loss: 0.9904
151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5819 - loss: 0.9903
154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5819 - loss: 0.9903
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5818 - loss: 0.9902
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5818 - loss: 0.9902
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5818 - loss: 0.9902
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5817 - loss: 0.9902
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5816 - loss: 0.9903
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5815 - loss: 0.9903
175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5814 - loss: 0.9904
178/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5813 - loss: 0.9904
181/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5812 - loss: 0.9905
184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5811 - loss: 0.9906
187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5811 - loss: 0.9906
189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5810 - loss: 0.9907
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5810 - loss: 0.9907
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5810 - loss: 0.9907
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5809 - loss: 0.9906
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5809 - loss: 0.9907
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5809 - loss: 0.9907
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5808 - loss: 0.9907
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5808 - loss: 0.9907
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5807 - loss: 0.9907
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5807 - loss: 0.9907
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5806 - loss: 0.9908
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5806 - loss: 0.9908
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5805 - loss: 0.9909
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5805 - loss: 0.9909
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5804 - loss: 0.9909
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5803 - loss: 0.9910
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5802 - loss: 0.9911
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5802 - loss: 0.9912
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5801 - loss: 0.9912
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5800 - loss: 0.9913
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5800 - loss: 0.9913
251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5799 - loss: 0.9914
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5798 - loss: 0.9915
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5798 - loss: 0.9915
260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5797 - loss: 0.9916
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5796 - loss: 0.9917
266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5796 - loss: 0.9917
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5795 - loss: 0.9918
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5794 - loss: 0.9918
275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5794 - loss: 0.9919
278/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5793 - loss: 0.9920
281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5793 - loss: 0.9920
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5792 - loss: 0.9921
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5791 - loss: 0.9922
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5791 - loss: 0.9923
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5790 - loss: 0.9923
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5790 - loss: 0.9924
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5789 - loss: 0.9925
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5789 - loss: 0.9925
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5788 - loss: 0.9926
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5787 - loss: 0.9927
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5787 - loss: 0.9928
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5786 - loss: 0.9928
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5785 - loss: 0.9929
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5785 - loss: 0.9930
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5784 - loss: 0.9931
325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5783 - loss: 0.9932
328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5783 - loss: 0.9933
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5782 - loss: 0.9934
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5782 - loss: 0.9934
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5781 - loss: 0.9935
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5781 - loss: 0.9936
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5781 - loss: 0.9936
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5780 - loss: 0.9937
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5780 - loss: 0.9938
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5779 - loss: 0.9939
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5779 - loss: 0.9939
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5779 - loss: 0.9940
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5778 - loss: 0.9940
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5778 - loss: 0.9941
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5778 - loss: 0.9942
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5777 - loss: 0.9942
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5777 - loss: 0.9943
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5777 - loss: 0.9943
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5776 - loss: 0.9944
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5776 - loss: 0.9944
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5776 - loss: 0.9945
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5775 - loss: 0.9945
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5775 - loss: 0.9946
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5775 - loss: 0.9946
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5775 - loss: 0.9946
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5774 - loss: 0.9947
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5774 - loss: 0.9947
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5774 - loss: 0.9948
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5774 - loss: 0.9948
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5774 - loss: 0.9948
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5774 - loss: 0.9949
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5773 - loss: 0.9949
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5773 - loss: 0.9949
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5773 - loss: 0.9950
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5773 - loss: 0.9950
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5772 - loss: 0.9951
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5772 - loss: 0.9951
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5772 - loss: 0.9952
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5772 - loss: 0.9952
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5771 - loss: 0.9953
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5771 - loss: 0.9953
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5771 - loss: 0.9954
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5771 - loss: 0.9954
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5770 - loss: 0.9955
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5770 - loss: 0.9955
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5770 - loss: 0.9956
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5769 - loss: 0.9956
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5769 - loss: 0.9956
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5769 - loss: 0.9957
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5769 - loss: 0.9957
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5768 - loss: 0.9958
472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5768 - loss: 0.9959
Epoch 26: val_accuracy did not improve from 0.58268
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5767 - loss: 0.9960 - val_accuracy: 0.5698 - val_loss: 1.0093 - learning_rate: 4.0000e-05
Epoch 27/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 162ms/step - accuracy: 0.4688 - loss: 1.1381
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5371 - loss: 1.0224
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5443 - loss: 1.0022
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5417 - loss: 1.0114
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5366 - loss: 1.0244
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5356 - loss: 1.0284
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5365 - loss: 1.0288
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5389 - loss: 1.0270
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5409 - loss: 1.0263
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5426 - loss: 1.0268
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5432 - loss: 1.0281
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5438 - loss: 1.0293
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5444 - loss: 1.0296
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5448 - loss: 1.0299
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5448 - loss: 1.0307
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5447 - loss: 1.0313
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5445 - loss: 1.0322
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5442 - loss: 1.0330
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5440 - loss: 1.0336
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5439 - loss: 1.0340
59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5439 - loss: 1.0342
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5439 - loss: 1.0345
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5441 - loss: 1.0345
68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5443 - loss: 1.0344
71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5444 - loss: 1.0345
74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5445 - loss: 1.0345
77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5447 - loss: 1.0345
80/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5448 - loss: 1.0345
83/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5449 - loss: 1.0345
86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5449 - loss: 1.0346
89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5449 - loss: 1.0347
92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5450 - loss: 1.0347
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5450 - loss: 1.0347
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5451 - loss: 1.0347
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5452 - loss: 1.0347
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5452 - loss: 1.0346
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Epoch 27: val_accuracy improved from 0.58268 to 0.58348, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5577 - loss: 1.0212 - val_accuracy: 0.5835 - val_loss: 0.9727 - learning_rate: 4.0000e-05
Epoch 28/40
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344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5771 - loss: 0.9966
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5771 - loss: 0.9967
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5770 - loss: 0.9968
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5770 - loss: 0.9968
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5769 - loss: 0.9969
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5769 - loss: 0.9970
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5768 - loss: 0.9970
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5768 - loss: 0.9971
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5768 - loss: 0.9971
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5767 - loss: 0.9972
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5767 - loss: 0.9972
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5766 - loss: 0.9973
376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5766 - loss: 0.9974
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5765 - loss: 0.9974
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5765 - loss: 0.9975
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5764 - loss: 0.9975
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5764 - loss: 0.9976
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5764 - loss: 0.9976
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5763 - loss: 0.9977
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5763 - loss: 0.9978
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9978
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9979
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9979
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5761 - loss: 0.9980
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5761 - loss: 0.9980
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5761 - loss: 0.9981
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5760 - loss: 0.9981
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5760 - loss: 0.9982
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5760 - loss: 0.9982
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5760 - loss: 0.9982
425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5759 - loss: 0.9983
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 0.9983
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 0.9983
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 0.9984
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9984
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9985
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9985
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9985
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9986
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9986
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9986
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9987
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5756 - loss: 0.9987
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5756 - loss: 0.9988
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5756 - loss: 0.9988
Epoch 28: val_accuracy did not improve from 0.58348
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5755 - loss: 0.9989 - val_accuracy: 0.5781 - val_loss: 0.9748 - learning_rate: 4.0000e-05
Epoch 29/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 160ms/step - accuracy: 0.5625 - loss: 1.1707
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5339 - loss: 1.1834
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5447 - loss: 1.1443
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5492 - loss: 1.1234
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5498 - loss: 1.1124
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5476 - loss: 1.1036
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5473 - loss: 1.0949
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5469 - loss: 1.0890
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5454 - loss: 1.0855
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5445 - loss: 1.0832
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5440 - loss: 1.0817
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5436 - loss: 1.0805
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5439 - loss: 1.0777
38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5445 - loss: 1.0746
41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5451 - loss: 1.0717
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5458 - loss: 1.0689
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5462 - loss: 1.0672
48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5465 - loss: 1.0657
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5470 - loss: 1.0635
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5477 - loss: 1.0612
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5482 - loss: 1.0593
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5486 - loss: 1.0575
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5490 - loss: 1.0560
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5493 - loss: 1.0548
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5496 - loss: 1.0535
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5500 - loss: 1.0522
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5503 - loss: 1.0511
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5505 - loss: 1.0500
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5508 - loss: 1.0491
84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5511 - loss: 1.0482
87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5514 - loss: 1.0472
90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5518 - loss: 1.0463
93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5521 - loss: 1.0454
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5524 - loss: 1.0445
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5527 - loss: 1.0437
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5530 - loss: 1.0429
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5533 - loss: 1.0421
108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5536 - loss: 1.0414
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5537 - loss: 1.0409
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5540 - loss: 1.0402
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5542 - loss: 1.0397
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5545 - loss: 1.0390
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5547 - loss: 1.0383
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5550 - loss: 1.0378
127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5553 - loss: 1.0372
130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5555 - loss: 1.0366
134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5558 - loss: 1.0359
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5561 - loss: 1.0354
140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5563 - loss: 1.0348
143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5565 - loss: 1.0343
146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5567 - loss: 1.0337
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5570 - loss: 1.0332
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5572 - loss: 1.0326
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5574 - loss: 1.0321
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5576 - loss: 1.0317
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5578 - loss: 1.0312
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5580 - loss: 1.0308
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5582 - loss: 1.0303
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5583 - loss: 1.0301
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5585 - loss: 1.0297
175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5586 - loss: 1.0293
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5588 - loss: 1.0290
179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5589 - loss: 1.0288
182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5590 - loss: 1.0284
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5592 - loss: 1.0280
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5594 - loss: 1.0277
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5595 - loss: 1.0273
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5597 - loss: 1.0269
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5598 - loss: 1.0266
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5600 - loss: 1.0263
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5601 - loss: 1.0260
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5602 - loss: 1.0258
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5603 - loss: 1.0255
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5604 - loss: 1.0252
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5605 - loss: 1.0250
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5606 - loss: 1.0247
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5607 - loss: 1.0245
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5608 - loss: 1.0243
226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5609 - loss: 1.0241
229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5610 - loss: 1.0239
232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5610 - loss: 1.0237
235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5611 - loss: 1.0236
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5612 - loss: 1.0234
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5612 - loss: 1.0232
244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5613 - loss: 1.0231
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5613 - loss: 1.0230
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5614 - loss: 1.0229
253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5614 - loss: 1.0227
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5615 - loss: 1.0226
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5615 - loss: 1.0226
262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5616 - loss: 1.0225
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5616 - loss: 1.0224
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5616 - loss: 1.0224
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5616 - loss: 1.0223
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5617 - loss: 1.0222
276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5617 - loss: 1.0222
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5617 - loss: 1.0221
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5617 - loss: 1.0220
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5618 - loss: 1.0220
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5618 - loss: 1.0220
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5618 - loss: 1.0219
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5618 - loss: 1.0219
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5619 - loss: 1.0218
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5619 - loss: 1.0218
302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5619 - loss: 1.0217
305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5619 - loss: 1.0217
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5620 - loss: 1.0216
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5620 - loss: 1.0216
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5620 - loss: 1.0215
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5621 - loss: 1.0215
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5621 - loss: 1.0214
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5621 - loss: 1.0214
324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5622 - loss: 1.0213
327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5622 - loss: 1.0212
330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5622 - loss: 1.0212
333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5623 - loss: 1.0211
336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5623 - loss: 1.0210
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5623 - loss: 1.0210
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0209
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0208
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5625 - loss: 1.0208
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5625 - loss: 1.0207
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5625 - loss: 1.0207
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5626 - loss: 1.0206
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5626 - loss: 1.0205
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5627 - loss: 1.0204
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5627 - loss: 1.0204
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5627 - loss: 1.0203
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5627 - loss: 1.0202
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5628 - loss: 1.0202
376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5628 - loss: 1.0201
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5628 - loss: 1.0201
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5629 - loss: 1.0200
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5629 - loss: 1.0199
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5629 - loss: 1.0199
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5629 - loss: 1.0198
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5630 - loss: 1.0198
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5630 - loss: 1.0197
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5630 - loss: 1.0196
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5630 - loss: 1.0196
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5630 - loss: 1.0195
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5630 - loss: 1.0195
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5631 - loss: 1.0194
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5631 - loss: 1.0194
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5631 - loss: 1.0193
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5631 - loss: 1.0193
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5631 - loss: 1.0193
424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5632 - loss: 1.0193
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5632 - loss: 1.0192
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5632 - loss: 1.0192
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5632 - loss: 1.0192
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5632 - loss: 1.0191
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0191
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0191
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0190
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0190
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0190
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0190
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0189
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0189
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5634 - loss: 1.0189
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5634 - loss: 1.0188
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5634 - loss: 1.0188
Epoch 29: val_accuracy did not improve from 0.58348
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5634 - loss: 1.0187 - val_accuracy: 0.5752 - val_loss: 0.9973 - learning_rate: 4.0000e-05
Epoch 30/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.6250 - loss: 0.9807
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6510 - loss: 0.8811
6/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.6323 - loss: 0.8955
9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.6188 - loss: 0.9165
12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.6101 - loss: 0.9329
15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6098 - loss: 0.9382
18/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6100 - loss: 0.9412
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6075 - loss: 0.9457
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6043 - loss: 0.9513
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6015 - loss: 0.9564
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5984 - loss: 0.9615
34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5968 - loss: 0.9639
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5958 - loss: 0.9662
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5949 - loss: 0.9677
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5941 - loss: 0.9691
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5931 - loss: 0.9706
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5923 - loss: 0.9721
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5918 - loss: 0.9731
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5913 - loss: 0.9742
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5910 - loss: 0.9750
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5907 - loss: 0.9758
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5904 - loss: 0.9767
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5902 - loss: 0.9773
68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5900 - loss: 0.9779
71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5897 - loss: 0.9788
74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5895 - loss: 0.9795
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5894 - loss: 0.9801
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5892 - loss: 0.9808
82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5889 - loss: 0.9816
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5886 - loss: 0.9824
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5882 - loss: 0.9831
91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5879 - loss: 0.9838
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5876 - loss: 0.9844
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5873 - loss: 0.9850
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5871 - loss: 0.9857
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5869 - loss: 0.9861
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456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5741 - loss: 1.0063
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5740 - loss: 1.0063
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5740 - loss: 1.0064
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5739 - loss: 1.0064
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5739 - loss: 1.0065
Epoch 30: val_accuracy did not improve from 0.58348
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5738 - loss: 1.0066 - val_accuracy: 0.5791 - val_loss: 0.9833 - learning_rate: 4.0000e-05
Epoch 31/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 163ms/step - accuracy: 0.4688 - loss: 1.0714
4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4948 - loss: 1.0772
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5080 - loss: 1.0682
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5181 - loss: 1.0610
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5309 - loss: 1.0494
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5402 - loss: 1.0408
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5457 - loss: 1.0364
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5495 - loss: 1.0330
24/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5516 - loss: 1.0316
26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5538 - loss: 1.0293
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5569 - loss: 1.0265
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5601 - loss: 1.0234
35/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5623 - loss: 1.0215
38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5636 - loss: 1.0200
41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5650 - loss: 1.0187
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5662 - loss: 1.0176
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5672 - loss: 1.0162
50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5678 - loss: 1.0151
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5680 - loss: 1.0146
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5680 - loss: 1.0141
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5682 - loss: 1.0135
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5684 - loss: 1.0128
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5686 - loss: 1.0121
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5688 - loss: 1.0116
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5689 - loss: 1.0113
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5690 - loss: 1.0109
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5691 - loss: 1.0108
79/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5691 - loss: 1.0106
82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5692 - loss: 1.0104
85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5693 - loss: 1.0103
87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5693 - loss: 1.0102
90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5694 - loss: 1.0101
93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5695 - loss: 1.0101
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5696 - loss: 1.0103
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5695 - loss: 1.0105
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5696 - loss: 1.0106
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5696 - loss: 1.0107
108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5696 - loss: 1.0108
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5696 - loss: 1.0108
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5696 - loss: 1.0109
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5697 - loss: 1.0108
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5698 - loss: 1.0108
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5699 - loss: 1.0108
126/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5700 - loss: 1.0108
129/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5700 - loss: 1.0109
131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5699 - loss: 1.0110
134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5699 - loss: 1.0111
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5699 - loss: 1.0112
140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5698 - loss: 1.0113
143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5698 - loss: 1.0114
146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5698 - loss: 1.0114
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5698 - loss: 1.0115
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5698 - loss: 1.0115
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5698 - loss: 1.0116
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5697 - loss: 1.0116
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5697 - loss: 1.0117
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5696 - loss: 1.0118
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5696 - loss: 1.0119
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5695 - loss: 1.0120
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5695 - loss: 1.0121
175/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5694 - loss: 1.0122
178/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5694 - loss: 1.0123
181/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0124
184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0125
187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5692 - loss: 1.0125
190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5692 - loss: 1.0126
193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5692 - loss: 1.0126
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0126
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0126
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0125
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0125
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0125
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5694 - loss: 1.0124
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5694 - loss: 1.0124
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5694 - loss: 1.0123
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5694 - loss: 1.0123
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5695 - loss: 1.0122
225/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5695 - loss: 1.0121
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5695 - loss: 1.0121
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5696 - loss: 1.0120
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5696 - loss: 1.0119
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5697 - loss: 1.0118
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5697 - loss: 1.0118
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5698 - loss: 1.0117
245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5698 - loss: 1.0117
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5698 - loss: 1.0116
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5699 - loss: 1.0115
253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5699 - loss: 1.0115
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5700 - loss: 1.0114
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5700 - loss: 1.0113
262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5700 - loss: 1.0113
265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5701 - loss: 1.0112
268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5701 - loss: 1.0111
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5702 - loss: 1.0110
274/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5702 - loss: 1.0110
277/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5703 - loss: 1.0109
280/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5703 - loss: 1.0108
283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5704 - loss: 1.0108
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5704 - loss: 1.0107
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5705 - loss: 1.0107
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5705 - loss: 1.0106
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5705 - loss: 1.0106
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5706 - loss: 1.0105
302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5706 - loss: 1.0105
305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5706 - loss: 1.0104
308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5707 - loss: 1.0104
311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5707 - loss: 1.0104
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5707 - loss: 1.0104
317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5707 - loss: 1.0103
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5708 - loss: 1.0103
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5708 - loss: 1.0103
325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5708 - loss: 1.0102
328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5709 - loss: 1.0102
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5709 - loss: 1.0102
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5709 - loss: 1.0101
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5710 - loss: 1.0101
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5710 - loss: 1.0101
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5710 - loss: 1.0100
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5710 - loss: 1.0100
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5710 - loss: 1.0100
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0100
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0099
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0099
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0099
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0099
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0098
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0098
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5711 - loss: 1.0098
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0098
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0097
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0097
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0097
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0096
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0096
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0096
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0095
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0095
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0095
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0094
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0094
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0094
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0094
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0094
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0093
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0093
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0093
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0093
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0092
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0092
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0092
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0092
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0091
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0091
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0091
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0091
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0091
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0090
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0090
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0090
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0090
Epoch 31: val_accuracy did not improve from 0.58348
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5714 - loss: 1.0090 - val_accuracy: 0.5754 - val_loss: 0.9934 - learning_rate: 4.0000e-05
Epoch 32/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 161ms/step - accuracy: 0.4062 - loss: 1.1242
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4759 - loss: 1.0784
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4866 - loss: 1.0723
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4964 - loss: 1.0618
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5049 - loss: 1.0526
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5158 - loss: 1.0398
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5233 - loss: 1.0311
22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5281 - loss: 1.0264
25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5313 - loss: 1.0232
28/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5337 - loss: 1.0211
31/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5354 - loss: 1.0202
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5363 - loss: 1.0201
36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5374 - loss: 1.0197
39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5378 - loss: 1.0201
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5380 - loss: 1.0207
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5382 - loss: 1.0211
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5386 - loss: 1.0217
50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5388 - loss: 1.0224
53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5395 - loss: 1.0225
56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5400 - loss: 1.0227
59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5404 - loss: 1.0230
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5409 - loss: 1.0231
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5415 - loss: 1.0231
68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5421 - loss: 1.0230
71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5428 - loss: 1.0229
74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5434 - loss: 1.0227
77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5440 - loss: 1.0224
80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5444 - loss: 1.0223
83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5447 - loss: 1.0223
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5448 - loss: 1.0223
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5450 - loss: 1.0225
91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5452 - loss: 1.0226
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5454 - loss: 1.0228
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5457 - loss: 1.0228
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5460 - loss: 1.0228
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5463 - loss: 1.0227
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5467 - loss: 1.0226
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5470 - loss: 1.0225
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5473 - loss: 1.0224
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5476 - loss: 1.0224
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5479 - loss: 1.0224
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5481 - loss: 1.0224
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5483 - loss: 1.0224
127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5485 - loss: 1.0225
130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5487 - loss: 1.0225
133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5488 - loss: 1.0225
136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5490 - loss: 1.0226
139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5492 - loss: 1.0225
142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5494 - loss: 1.0224
145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5496 - loss: 1.0223
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5497 - loss: 1.0223
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5499 - loss: 1.0222
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5500 - loss: 1.0222
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5502 - loss: 1.0221
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5503 - loss: 1.0220
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5505 - loss: 1.0219
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5506 - loss: 1.0219
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5507 - loss: 1.0218
170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5509 - loss: 1.0217
173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5510 - loss: 1.0216
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5512 - loss: 1.0215
179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5514 - loss: 1.0214
182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5515 - loss: 1.0213
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5517 - loss: 1.0212
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5518 - loss: 1.0211
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5520 - loss: 1.0210
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5521 - loss: 1.0209
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5522 - loss: 1.0208
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5524 - loss: 1.0207
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5525 - loss: 1.0206
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5527 - loss: 1.0205
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5529 - loss: 1.0204
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5530 - loss: 1.0203
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5532 - loss: 1.0202
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5533 - loss: 1.0201
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5535 - loss: 1.0200
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5536 - loss: 1.0199
224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5537 - loss: 1.0198
227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5538 - loss: 1.0197
230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5540 - loss: 1.0196
233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5542 - loss: 1.0194
236/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5543 - loss: 1.0192
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5545 - loss: 1.0191
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5546 - loss: 1.0190
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5547 - loss: 1.0188
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5549 - loss: 1.0187
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5551 - loss: 1.0185
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5553 - loss: 1.0183
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5554 - loss: 1.0181
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5555 - loss: 1.0180
260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5557 - loss: 1.0179
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5558 - loss: 1.0177
266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5560 - loss: 1.0176
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5561 - loss: 1.0175
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5562 - loss: 1.0174
275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5563 - loss: 1.0173
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5565 - loss: 1.0172
281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5566 - loss: 1.0170
283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5567 - loss: 1.0170
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5568 - loss: 1.0169
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5569 - loss: 1.0168
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5570 - loss: 1.0166
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5571 - loss: 1.0166
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5572 - loss: 1.0165
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5574 - loss: 1.0164
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5575 - loss: 1.0163
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5576 - loss: 1.0162
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5576 - loss: 1.0162
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5577 - loss: 1.0161
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5578 - loss: 1.0160
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5579 - loss: 1.0160
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5580 - loss: 1.0159
325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5581 - loss: 1.0158
327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5581 - loss: 1.0158
330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5582 - loss: 1.0157
333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5583 - loss: 1.0157
336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5583 - loss: 1.0156
339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5584 - loss: 1.0156
342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5585 - loss: 1.0156
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5585 - loss: 1.0155
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5585 - loss: 1.0155
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5586 - loss: 1.0155
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5586 - loss: 1.0155
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5587 - loss: 1.0155
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5588 - loss: 1.0154
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5588 - loss: 1.0154
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5588 - loss: 1.0153
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5589 - loss: 1.0153
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5589 - loss: 1.0152
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5590 - loss: 1.0152
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5590 - loss: 1.0152
375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5591 - loss: 1.0151
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5591 - loss: 1.0151
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5592 - loss: 1.0150
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5592 - loss: 1.0150
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5592 - loss: 1.0150
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5593 - loss: 1.0149
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5593 - loss: 1.0149
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5594 - loss: 1.0148
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5594 - loss: 1.0148
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5595 - loss: 1.0148
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5595 - loss: 1.0148
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5595 - loss: 1.0147
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5595 - loss: 1.0147
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5596 - loss: 1.0147
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5596 - loss: 1.0146
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5596 - loss: 1.0146
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5597 - loss: 1.0146
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5597 - loss: 1.0145
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5597 - loss: 1.0145
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5598 - loss: 1.0145
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5598 - loss: 1.0144
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5598 - loss: 1.0144
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5599 - loss: 1.0144
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5599 - loss: 1.0143
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5600 - loss: 1.0143
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5600 - loss: 1.0143
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5600 - loss: 1.0142
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5601 - loss: 1.0142
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5601 - loss: 1.0141
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5601 - loss: 1.0141
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5601 - loss: 1.0141
Epoch 32: ReduceLROnPlateau reducing learning rate to 1e-05.
Epoch 32: val_accuracy did not improve from 0.58348
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5602 - loss: 1.0140 - val_accuracy: 0.5767 - val_loss: 0.9849 - learning_rate: 4.0000e-05
Epoch 33/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 166ms/step - accuracy: 0.5000 - loss: 1.0800
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5853 - loss: 1.0019
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5955 - loss: 0.9846
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6001 - loss: 0.9782
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6013 - loss: 0.9753
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5991 - loss: 0.9776
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5960 - loss: 0.9810
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5935 - loss: 0.9832
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5921 - loss: 0.9848
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5917 - loss: 0.9851
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5913 - loss: 0.9856
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5912 - loss: 0.9859
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5910 - loss: 0.9864
39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5903 - loss: 0.9872
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5892 - loss: 0.9886
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5881 - loss: 0.9901
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5870 - loss: 0.9912
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5861 - loss: 0.9921
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5855 - loss: 0.9926
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5852 - loss: 0.9932
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5849 - loss: 0.9935
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5846 - loss: 0.9938
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5841 - loss: 0.9946
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5835 - loss: 0.9954
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5829 - loss: 0.9961
75/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5824 - loss: 0.9966
78/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5819 - loss: 0.9970
81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5813 - loss: 0.9973
84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5808 - loss: 0.9976
86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5805 - loss: 0.9978
89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5801 - loss: 0.9980
92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5797 - loss: 0.9983
95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5793 - loss: 0.9985
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5788 - loss: 0.9987
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5785 - loss: 0.9988
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5782 - loss: 0.9991
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5778 - loss: 0.9992
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5776 - loss: 0.9993
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5773 - loss: 0.9993
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5771 - loss: 0.9994
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5770 - loss: 0.9994
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5769 - loss: 0.9994
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5767 - loss: 0.9993
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5766 - loss: 0.9993
128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5766 - loss: 0.9993
131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5765 - loss: 0.9992
134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5764 - loss: 0.9992
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5764 - loss: 0.9992
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151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9987
154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9986
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9985
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9984
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9984
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169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9982
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174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9982
178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5762 - loss: 0.9981
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184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5762 - loss: 0.9979
187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5762 - loss: 0.9979
190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5762 - loss: 0.9978
193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5762 - loss: 0.9977
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5762 - loss: 0.9976
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222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5764 - loss: 0.9972
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5764 - loss: 0.9972
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5764 - loss: 0.9972
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232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5764 - loss: 0.9971
235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5764 - loss: 0.9971
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9970
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9970
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9970
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9968
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264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9968
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9968
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9968
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9968
274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969
276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969
281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5765 - loss: 0.9969
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5765 - loss: 0.9969
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5765 - loss: 0.9969
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5765 - loss: 0.9969
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5765 - loss: 0.9969
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5765 - loss: 0.9970
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5764 - loss: 0.9970
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5764 - loss: 0.9970
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5764 - loss: 0.9971
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5764 - loss: 0.9971
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5763 - loss: 0.9972
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5763 - loss: 0.9973
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5763 - loss: 0.9973
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5762 - loss: 0.9974
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5762 - loss: 0.9974
324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5762 - loss: 0.9975
326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5762 - loss: 0.9975
329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5761 - loss: 0.9976
332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5761 - loss: 0.9977
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5761 - loss: 0.9977
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5761 - loss: 0.9978
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5760 - loss: 0.9978
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5760 - loss: 0.9979
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5760 - loss: 0.9979
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5760 - loss: 0.9979
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5760 - loss: 0.9980
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5760 - loss: 0.9980
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5759 - loss: 0.9981
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5759 - loss: 0.9981
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5759 - loss: 0.9982
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5759 - loss: 0.9982
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5758 - loss: 0.9983
374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5758 - loss: 0.9983
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5758 - loss: 0.9984
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5758 - loss: 0.9984
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9985
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9985
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9985
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9986
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9986
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9987
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9987
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9988
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5755 - loss: 0.9988
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5755 - loss: 0.9988
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5755 - loss: 0.9989
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5755 - loss: 0.9989
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5754 - loss: 0.9989
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5754 - loss: 0.9990
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5754 - loss: 0.9990
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5754 - loss: 0.9991
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5753 - loss: 0.9991
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5753 - loss: 0.9992
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5753 - loss: 0.9992
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5752 - loss: 0.9993
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5752 - loss: 0.9993
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5752 - loss: 0.9994
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5752 - loss: 0.9994
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5751 - loss: 0.9994
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5751 - loss: 0.9995
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5751 - loss: 0.9995
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5751 - loss: 0.9995
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5750 - loss: 0.9996
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5750 - loss: 0.9996
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5750 - loss: 0.9997
Epoch 33: val_accuracy did not improve from 0.58348
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5749 - loss: 0.9998 - val_accuracy: 0.5799 - val_loss: 0.9842 - learning_rate: 1.0000e-05
Epoch 34/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:20 171ms/step - accuracy: 0.5312 - loss: 1.1022
3/473 ━━━━━━━━━━━━━━━━━━━━ 16s 35ms/step - accuracy: 0.5469 - loss: 1.0521
6/473 ━━━━━━━━━━━━━━━━━━━━ 11s 26ms/step - accuracy: 0.5495 - loss: 1.0595
9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5619 - loss: 1.0464
12/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5664 - loss: 1.0368
14/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5672 - loss: 1.0333
17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5681 - loss: 1.0304
20/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5689 - loss: 1.0267
23/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5703 - loss: 1.0237
26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5717 - loss: 1.0212
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5730 - loss: 1.0192
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5739 - loss: 1.0175
34/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5744 - loss: 1.0167
37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5747 - loss: 1.0163
41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5748 - loss: 1.0160
44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5749 - loss: 1.0155
47/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5751 - loss: 1.0140
50/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5755 - loss: 1.0124
52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5758 - loss: 1.0113
55/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5764 - loss: 1.0098
58/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5768 - loss: 1.0082
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5771 - loss: 1.0070
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5771 - loss: 1.0062
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5770 - loss: 1.0057
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5770 - loss: 1.0053
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5771 - loss: 1.0047
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5770 - loss: 1.0042
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5770 - loss: 1.0038
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5770 - loss: 1.0033
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5771 - loss: 1.0028
87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5772 - loss: 1.0023
90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5772 - loss: 1.0018
92/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5772 - loss: 1.0015
95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5771 - loss: 1.0013
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5770 - loss: 1.0012
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5768 - loss: 1.0010
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5767 - loss: 1.0009
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5765 - loss: 1.0007
108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5764 - loss: 1.0005
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5763 - loss: 1.0003
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5762 - loss: 1.0000
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5761 - loss: 0.9997
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5760 - loss: 0.9995
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5759 - loss: 0.9993
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5757 - loss: 0.9991
129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5756 - loss: 0.9990
132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5755 - loss: 0.9988
135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5754 - loss: 0.9987
138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5753 - loss: 0.9986
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5753 - loss: 0.9985
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9983
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9982
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9980
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9979
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9978
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9977
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9976
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9975
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9975
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5751 - loss: 0.9975
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5751 - loss: 0.9974
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5751 - loss: 0.9974
180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5750 - loss: 0.9974
182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5750 - loss: 0.9973
184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5750 - loss: 0.9973
187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5750 - loss: 0.9973
190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5750 - loss: 0.9972
193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5750 - loss: 0.9971
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5750 - loss: 0.9971
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5749 - loss: 0.9970
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5749 - loss: 0.9969
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5749 - loss: 0.9969
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5749 - loss: 0.9968
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5749 - loss: 0.9968
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5749 - loss: 0.9968
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5749 - loss: 0.9967
218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5749 - loss: 0.9967
221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5750 - loss: 0.9967
224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5750 - loss: 0.9967
226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5750 - loss: 0.9967
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5750 - loss: 0.9967
231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5750 - loss: 0.9967
234/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5750 - loss: 0.9967
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5750 - loss: 0.9967
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5750 - loss: 0.9967
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5750 - loss: 0.9967
245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5750 - loss: 0.9967
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5750 - loss: 0.9967
251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5750 - loss: 0.9967
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5750 - loss: 0.9967
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5750 - loss: 0.9967
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5750 - loss: 0.9967
262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5750 - loss: 0.9967
265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5750 - loss: 0.9967
268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5750 - loss: 0.9967
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5750 - loss: 0.9967
274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5751 - loss: 0.9967
277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5751 - loss: 0.9967
280/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5751 - loss: 0.9967
283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5751 - loss: 0.9967
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5751 - loss: 0.9967
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5751 - loss: 0.9967
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5751 - loss: 0.9967
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5751 - loss: 0.9967
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5751 - loss: 0.9966
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5751 - loss: 0.9966
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5751 - loss: 0.9966
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5752 - loss: 0.9966
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5752 - loss: 0.9966
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5752 - loss: 0.9966
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5752 - loss: 0.9966
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5752 - loss: 0.9966
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5752 - loss: 0.9966
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5752 - loss: 0.9966
325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5753 - loss: 0.9966
328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5753 - loss: 0.9966
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5753 - loss: 0.9966
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5753 - loss: 0.9966
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5754 - loss: 0.9965
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5754 - loss: 0.9965
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5754 - loss: 0.9965
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5754 - loss: 0.9965
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5754 - loss: 0.9965
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5754 - loss: 0.9965
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5754 - loss: 0.9965
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5754 - loss: 0.9966
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5755 - loss: 0.9966
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5755 - loss: 0.9966
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5755 - loss: 0.9966
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5755 - loss: 0.9966
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5755 - loss: 0.9966
376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5755 - loss: 0.9966
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5755 - loss: 0.9966
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5755 - loss: 0.9966
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5755 - loss: 0.9966
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9966
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9965
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9965
397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9965
400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9965
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9965
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9965
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9965
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9964
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9964
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9964
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9964
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9964
424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9964
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9964
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439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9965
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9965
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9965
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9965
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459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9965
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9965
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9966
Epoch 34: val_accuracy did not improve from 0.58348
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5758 - loss: 0.9966 - val_accuracy: 0.5799 - val_loss: 0.9852 - learning_rate: 1.0000e-05
Epoch 34: early stopping
Restoring model weights from the end of the best epoch: 27.
Plotting the Training and Validation Accuracies¶
plt.plot(history_resnet.history["accuracy"])
plt.plot(history_resnet.history["val_accuracy"])
plt.title("ResNet50V2 Model accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train", "validation"], loc="upper left")
plt.show()
Evaluating the ResNet Model¶
# Calculate the number of steps for the entire test set to be processed
test_steps = test_generator_resnet.samples // batch_size
# If the number of samples isn't a multiple of the batch size,
# you have one more batch with the remaining samples
if test_generator_resnet.samples % batch_size > 0:
test_steps += 1
# Evaluating the model on the test set
evaluation_results = new_resnet_model.evaluate(test_generator_resnet, steps=test_steps)
print(f"Loss: {evaluation_results[0]}, Accuracy: {evaluation_results[1]}")
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6250 - loss: 0.9257
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5740 - loss: 0.9644
Loss: 0.9330559968948364, Accuracy: 0.578125
Plotting Confusion Matrix¶
pred_probabilities = new_resnet_model.predict(test_generator_resnet, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)
# Getting the true labels from the generator
y_true = test_generator_resnet.classes
# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))
# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("ResNet50V2 Model Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 2s 866ms/step
4/4 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step
precision recall f1-score support
happy 0.57 0.62 0.60 32
neutral 0.40 0.53 0.45 32
sad 0.71 0.38 0.49 32
surprise 0.76 0.78 0.77 32
accuracy 0.58 128
macro avg 0.61 0.58 0.58 128
weighted avg 0.61 0.58 0.58 128
Observations and Insights:
- The ResNet50V2 model was customized for the task, using 1,453,568 total parameters. We cut the original model at the 'conv3_block4_out' layer, as we are dealing with low-resolution images
- The test accuracy achieved was 57.81%, indicating a modest performance in predicting the facial emotions on unseen images.
- The model had varying success with different emotions, performing best on 'surprise' with an f1-score of 0.77, and least effectively on 'neutral', with a lower f1-score of 0.45.
- We have chosen also the ResNet50V2 over the other models from this family (ResNet101V2 and ResNet152V2) for the size being smaller and the computational efficiency of the model, which we found more suitable for the task we had to accomplish.
EfficientNet Model¶
backend.clear_session()
# Fixing the seed for random number generators so that we can ensure we receive the same output everytime
np.random.seed(42)
random.seed(42)
tf.random.set_seed(42)
efficient_model = EfficientNetV2B0(
weights="imagenet", include_top=False, input_shape=(img_width, img_height, color_layers)
)
# Making all the layers of the efficient_model model non-trainable. i.e. freezing them
for layer in efficient_model.layers:
layer.trainable = False
efficient_model.summary()
Model: "efficientnetv2-b0"
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ │ input_layer │ (None, 48, 48, 3) │ 0 │ - │ │ (InputLayer) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ rescaling │ (None, 48, 48, 3) │ 0 │ input_layer[0][0] │ │ (Rescaling) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ normalization │ (None, 48, 48, 3) │ 0 │ rescaling[0][0] │ │ (Normalization) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ stem_conv (Conv2D) │ (None, 24, 24, │ 864 │ normalization[0]… │ │ │ 32) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ stem_bn │ (None, 24, 24, │ 128 │ stem_conv[0][0] │ │ (BatchNormalizatio… │ 32) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ stem_activation │ (None, 24, 24, │ 0 │ stem_bn[0][0] │ │ (Activation) │ 32) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block1a_project_co… │ (None, 24, 24, │ 4,608 │ stem_activation[… │ │ (Conv2D) │ 16) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block1a_project_bn │ (None, 24, 24, │ 64 │ block1a_project_… │ │ (BatchNormalizatio… │ 16) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block1a_project_ac… │ (None, 24, 24, │ 0 │ block1a_project_… │ │ (Activation) │ 16) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block2a_expand_conv │ (None, 12, 12, │ 9,216 │ block1a_project_… │ │ (Conv2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block2a_expand_bn │ (None, 12, 12, │ 256 │ block2a_expand_c… │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block2a_expand_act… │ (None, 12, 12, │ 0 │ block2a_expand_b… │ │ (Activation) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block2a_project_co… │ (None, 12, 12, │ 2,048 │ block2a_expand_a… │ │ (Conv2D) │ 32) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block2a_project_bn │ (None, 12, 12, │ 128 │ block2a_project_… │ │ (BatchNormalizatio… │ 32) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block2b_expand_conv │ (None, 12, 12, │ 36,864 │ block2a_project_… │ │ (Conv2D) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block2b_expand_bn │ (None, 12, 12, │ 512 │ block2b_expand_c… │ │ (BatchNormalizatio… │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block2b_expand_act… │ (None, 12, 12, │ 0 │ block2b_expand_b… │ │ (Activation) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block2b_project_co… │ (None, 12, 12, │ 4,096 │ block2b_expand_a… │ │ (Conv2D) │ 32) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block2b_project_bn │ (None, 12, 12, │ 128 │ block2b_project_… │ │ (BatchNormalizatio… │ 32) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block2b_drop │ (None, 12, 12, │ 0 │ block2b_project_… │ │ (Dropout) │ 32) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block2b_add (Add) │ (None, 12, 12, │ 0 │ block2b_drop[0][… │ │ │ 32) │ │ block2a_project_… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block3a_expand_conv │ (None, 6, 6, 128) │ 36,864 │ block2b_add[0][0] │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block3a_expand_bn │ (None, 6, 6, 128) │ 512 │ block3a_expand_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block3a_expand_act… │ (None, 6, 6, 128) │ 0 │ block3a_expand_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block3a_project_co… │ (None, 6, 6, 48) │ 6,144 │ block3a_expand_a… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block3a_project_bn │ (None, 6, 6, 48) │ 192 │ block3a_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block3b_expand_conv │ (None, 6, 6, 192) │ 82,944 │ block3a_project_… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block3b_expand_bn │ (None, 6, 6, 192) │ 768 │ block3b_expand_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block3b_expand_act… │ (None, 6, 6, 192) │ 0 │ block3b_expand_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block3b_project_co… │ (None, 6, 6, 48) │ 9,216 │ block3b_expand_a… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block3b_project_bn │ (None, 6, 6, 48) │ 192 │ block3b_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block3b_drop │ (None, 6, 6, 48) │ 0 │ block3b_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block3b_add (Add) │ (None, 6, 6, 48) │ 0 │ block3b_drop[0][… │ │ │ │ │ block3a_project_… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4a_expand_conv │ (None, 6, 6, 192) │ 9,216 │ block3b_add[0][0] │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4a_expand_bn │ (None, 6, 6, 192) │ 768 │ block4a_expand_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4a_expand_act… │ (None, 6, 6, 192) │ 0 │ block4a_expand_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4a_dwconv2 │ (None, 3, 3, 192) │ 1,728 │ block4a_expand_a… │ │ (DepthwiseConv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4a_bn │ (None, 3, 3, 192) │ 768 │ block4a_dwconv2[… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4a_activation │ (None, 3, 3, 192) │ 0 │ block4a_bn[0][0] │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4a_se_squeeze │ (None, 192) │ 0 │ block4a_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4a_se_reshape │ (None, 1, 1, 192) │ 0 │ block4a_se_squee… │ │ (Reshape) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4a_se_reduce │ (None, 1, 1, 12) │ 2,316 │ block4a_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4a_se_expand │ (None, 1, 1, 192) │ 2,496 │ block4a_se_reduc… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4a_se_excite │ (None, 3, 3, 192) │ 0 │ block4a_activati… │ │ (Multiply) │ │ │ block4a_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4a_project_co… │ (None, 3, 3, 96) │ 18,432 │ block4a_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4a_project_bn │ (None, 3, 3, 96) │ 384 │ block4a_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_expand_conv │ (None, 3, 3, 384) │ 36,864 │ block4a_project_… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_expand_bn │ (None, 3, 3, 384) │ 1,536 │ block4b_expand_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_expand_act… │ (None, 3, 3, 384) │ 0 │ block4b_expand_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_dwconv2 │ (None, 3, 3, 384) │ 3,456 │ block4b_expand_a… │ │ (DepthwiseConv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_bn │ (None, 3, 3, 384) │ 1,536 │ block4b_dwconv2[… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_activation │ (None, 3, 3, 384) │ 0 │ block4b_bn[0][0] │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_se_squeeze │ (None, 384) │ 0 │ block4b_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_se_reshape │ (None, 1, 1, 384) │ 0 │ block4b_se_squee… │ │ (Reshape) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_se_reduce │ (None, 1, 1, 24) │ 9,240 │ block4b_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_se_expand │ (None, 1, 1, 384) │ 9,600 │ block4b_se_reduc… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_se_excite │ (None, 3, 3, 384) │ 0 │ block4b_activati… │ │ (Multiply) │ │ │ block4b_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_project_co… │ (None, 3, 3, 96) │ 36,864 │ block4b_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_project_bn │ (None, 3, 3, 96) │ 384 │ block4b_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_drop │ (None, 3, 3, 96) │ 0 │ block4b_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4b_add (Add) │ (None, 3, 3, 96) │ 0 │ block4b_drop[0][… │ │ │ │ │ block4a_project_… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_expand_conv │ (None, 3, 3, 384) │ 36,864 │ block4b_add[0][0] │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_expand_bn │ (None, 3, 3, 384) │ 1,536 │ block4c_expand_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_expand_act… │ (None, 3, 3, 384) │ 0 │ block4c_expand_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_dwconv2 │ (None, 3, 3, 384) │ 3,456 │ block4c_expand_a… │ │ (DepthwiseConv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_bn │ (None, 3, 3, 384) │ 1,536 │ block4c_dwconv2[… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_activation │ (None, 3, 3, 384) │ 0 │ block4c_bn[0][0] │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_se_squeeze │ (None, 384) │ 0 │ block4c_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_se_reshape │ (None, 1, 1, 384) │ 0 │ block4c_se_squee… │ │ (Reshape) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_se_reduce │ (None, 1, 1, 24) │ 9,240 │ block4c_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_se_expand │ (None, 1, 1, 384) │ 9,600 │ block4c_se_reduc… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_se_excite │ (None, 3, 3, 384) │ 0 │ block4c_activati… │ │ (Multiply) │ │ │ block4c_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_project_co… │ (None, 3, 3, 96) │ 36,864 │ block4c_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_project_bn │ (None, 3, 3, 96) │ 384 │ block4c_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_drop │ (None, 3, 3, 96) │ 0 │ block4c_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block4c_add (Add) │ (None, 3, 3, 96) │ 0 │ block4c_drop[0][… │ │ │ │ │ block4b_add[0][0] │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5a_expand_conv │ (None, 3, 3, 576) │ 55,296 │ block4c_add[0][0] │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5a_expand_bn │ (None, 3, 3, 576) │ 2,304 │ block5a_expand_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5a_expand_act… │ (None, 3, 3, 576) │ 0 │ block5a_expand_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5a_dwconv2 │ (None, 3, 3, 576) │ 5,184 │ block5a_expand_a… │ │ (DepthwiseConv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5a_bn │ (None, 3, 3, 576) │ 2,304 │ block5a_dwconv2[… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5a_activation │ (None, 3, 3, 576) │ 0 │ block5a_bn[0][0] │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5a_se_squeeze │ (None, 576) │ 0 │ block5a_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5a_se_reshape │ (None, 1, 1, 576) │ 0 │ block5a_se_squee… │ │ (Reshape) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5a_se_reduce │ (None, 1, 1, 24) │ 13,848 │ block5a_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5a_se_expand │ (None, 1, 1, 576) │ 14,400 │ block5a_se_reduc… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5a_se_excite │ (None, 3, 3, 576) │ 0 │ block5a_activati… │ │ (Multiply) │ │ │ block5a_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5a_project_co… │ (None, 3, 3, 112) │ 64,512 │ block5a_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5a_project_bn │ (None, 3, 3, 112) │ 448 │ block5a_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_expand_conv │ (None, 3, 3, 672) │ 75,264 │ block5a_project_… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_expand_bn │ (None, 3, 3, 672) │ 2,688 │ block5b_expand_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_expand_act… │ (None, 3, 3, 672) │ 0 │ block5b_expand_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_dwconv2 │ (None, 3, 3, 672) │ 6,048 │ block5b_expand_a… │ │ (DepthwiseConv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_bn │ (None, 3, 3, 672) │ 2,688 │ block5b_dwconv2[… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_activation │ (None, 3, 3, 672) │ 0 │ block5b_bn[0][0] │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_se_squeeze │ (None, 672) │ 0 │ block5b_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_se_reshape │ (None, 1, 1, 672) │ 0 │ block5b_se_squee… │ │ (Reshape) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_se_reduce │ (None, 1, 1, 28) │ 18,844 │ block5b_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_se_expand │ (None, 1, 1, 672) │ 19,488 │ block5b_se_reduc… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_se_excite │ (None, 3, 3, 672) │ 0 │ block5b_activati… │ │ (Multiply) │ │ │ block5b_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_project_co… │ (None, 3, 3, 112) │ 75,264 │ block5b_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_project_bn │ (None, 3, 3, 112) │ 448 │ block5b_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_drop │ (None, 3, 3, 112) │ 0 │ block5b_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5b_add (Add) │ (None, 3, 3, 112) │ 0 │ block5b_drop[0][… │ │ │ │ │ block5a_project_… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_expand_conv │ (None, 3, 3, 672) │ 75,264 │ block5b_add[0][0] │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_expand_bn │ (None, 3, 3, 672) │ 2,688 │ block5c_expand_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_expand_act… │ (None, 3, 3, 672) │ 0 │ block5c_expand_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_dwconv2 │ (None, 3, 3, 672) │ 6,048 │ block5c_expand_a… │ │ (DepthwiseConv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_bn │ (None, 3, 3, 672) │ 2,688 │ block5c_dwconv2[… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_activation │ (None, 3, 3, 672) │ 0 │ block5c_bn[0][0] │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_se_squeeze │ (None, 672) │ 0 │ block5c_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_se_reshape │ (None, 1, 1, 672) │ 0 │ block5c_se_squee… │ │ (Reshape) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_se_reduce │ (None, 1, 1, 28) │ 18,844 │ block5c_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_se_expand │ (None, 1, 1, 672) │ 19,488 │ block5c_se_reduc… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_se_excite │ (None, 3, 3, 672) │ 0 │ block5c_activati… │ │ (Multiply) │ │ │ block5c_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_project_co… │ (None, 3, 3, 112) │ 75,264 │ block5c_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_project_bn │ (None, 3, 3, 112) │ 448 │ block5c_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_drop │ (None, 3, 3, 112) │ 0 │ block5c_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5c_add (Add) │ (None, 3, 3, 112) │ 0 │ block5c_drop[0][… │ │ │ │ │ block5b_add[0][0] │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_expand_conv │ (None, 3, 3, 672) │ 75,264 │ block5c_add[0][0] │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_expand_bn │ (None, 3, 3, 672) │ 2,688 │ block5d_expand_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_expand_act… │ (None, 3, 3, 672) │ 0 │ block5d_expand_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_dwconv2 │ (None, 3, 3, 672) │ 6,048 │ block5d_expand_a… │ │ (DepthwiseConv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_bn │ (None, 3, 3, 672) │ 2,688 │ block5d_dwconv2[… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_activation │ (None, 3, 3, 672) │ 0 │ block5d_bn[0][0] │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_se_squeeze │ (None, 672) │ 0 │ block5d_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_se_reshape │ (None, 1, 1, 672) │ 0 │ block5d_se_squee… │ │ (Reshape) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_se_reduce │ (None, 1, 1, 28) │ 18,844 │ block5d_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_se_expand │ (None, 1, 1, 672) │ 19,488 │ block5d_se_reduc… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_se_excite │ (None, 3, 3, 672) │ 0 │ block5d_activati… │ │ (Multiply) │ │ │ block5d_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_project_co… │ (None, 3, 3, 112) │ 75,264 │ block5d_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_project_bn │ (None, 3, 3, 112) │ 448 │ block5d_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_drop │ (None, 3, 3, 112) │ 0 │ block5d_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5d_add (Add) │ (None, 3, 3, 112) │ 0 │ block5d_drop[0][… │ │ │ │ │ block5c_add[0][0] │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_expand_conv │ (None, 3, 3, 672) │ 75,264 │ block5d_add[0][0] │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_expand_bn │ (None, 3, 3, 672) │ 2,688 │ block5e_expand_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_expand_act… │ (None, 3, 3, 672) │ 0 │ block5e_expand_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_dwconv2 │ (None, 3, 3, 672) │ 6,048 │ block5e_expand_a… │ │ (DepthwiseConv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_bn │ (None, 3, 3, 672) │ 2,688 │ block5e_dwconv2[… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_activation │ (None, 3, 3, 672) │ 0 │ block5e_bn[0][0] │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_se_squeeze │ (None, 672) │ 0 │ block5e_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_se_reshape │ (None, 1, 1, 672) │ 0 │ block5e_se_squee… │ │ (Reshape) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_se_reduce │ (None, 1, 1, 28) │ 18,844 │ block5e_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_se_expand │ (None, 1, 1, 672) │ 19,488 │ block5e_se_reduc… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_se_excite │ (None, 3, 3, 672) │ 0 │ block5e_activati… │ │ (Multiply) │ │ │ block5e_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_project_co… │ (None, 3, 3, 112) │ 75,264 │ block5e_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_project_bn │ (None, 3, 3, 112) │ 448 │ block5e_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_drop │ (None, 3, 3, 112) │ 0 │ block5e_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block5e_add (Add) │ (None, 3, 3, 112) │ 0 │ block5e_drop[0][… │ │ │ │ │ block5d_add[0][0] │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6a_expand_conv │ (None, 3, 3, 672) │ 75,264 │ block5e_add[0][0] │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6a_expand_bn │ (None, 3, 3, 672) │ 2,688 │ block6a_expand_c… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6a_expand_act… │ (None, 3, 3, 672) │ 0 │ block6a_expand_b… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6a_dwconv2 │ (None, 2, 2, 672) │ 6,048 │ block6a_expand_a… │ │ (DepthwiseConv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6a_bn │ (None, 2, 2, 672) │ 2,688 │ block6a_dwconv2[… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6a_activation │ (None, 2, 2, 672) │ 0 │ block6a_bn[0][0] │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6a_se_squeeze │ (None, 672) │ 0 │ block6a_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6a_se_reshape │ (None, 1, 1, 672) │ 0 │ block6a_se_squee… │ │ (Reshape) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6a_se_reduce │ (None, 1, 1, 28) │ 18,844 │ block6a_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6a_se_expand │ (None, 1, 1, 672) │ 19,488 │ block6a_se_reduc… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6a_se_excite │ (None, 2, 2, 672) │ 0 │ block6a_activati… │ │ (Multiply) │ │ │ block6a_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6a_project_co… │ (None, 2, 2, 192) │ 129,024 │ block6a_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6a_project_bn │ (None, 2, 2, 192) │ 768 │ block6a_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_expand_conv │ (None, 2, 2, │ 221,184 │ block6a_project_… │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_expand_bn │ (None, 2, 2, │ 4,608 │ block6b_expand_c… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_expand_act… │ (None, 2, 2, │ 0 │ block6b_expand_b… │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_dwconv2 │ (None, 2, 2, │ 10,368 │ block6b_expand_a… │ │ (DepthwiseConv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_bn │ (None, 2, 2, │ 4,608 │ block6b_dwconv2[… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_activation │ (None, 2, 2, │ 0 │ block6b_bn[0][0] │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_se_squeeze │ (None, 1152) │ 0 │ block6b_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_se_reshape │ (None, 1, 1, │ 0 │ block6b_se_squee… │ │ (Reshape) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block6b_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_se_expand │ (None, 1, 1, │ 56,448 │ block6b_se_reduc… │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_se_excite │ (None, 2, 2, │ 0 │ block6b_activati… │ │ (Multiply) │ 1152) │ │ block6b_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_project_co… │ (None, 2, 2, 192) │ 221,184 │ block6b_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_project_bn │ (None, 2, 2, 192) │ 768 │ block6b_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_drop │ (None, 2, 2, 192) │ 0 │ block6b_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6b_add (Add) │ (None, 2, 2, 192) │ 0 │ block6b_drop[0][… │ │ │ │ │ block6a_project_… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_expand_conv │ (None, 2, 2, │ 221,184 │ block6b_add[0][0] │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_expand_bn │ (None, 2, 2, │ 4,608 │ block6c_expand_c… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_expand_act… │ (None, 2, 2, │ 0 │ block6c_expand_b… │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_dwconv2 │ (None, 2, 2, │ 10,368 │ block6c_expand_a… │ │ (DepthwiseConv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_bn │ (None, 2, 2, │ 4,608 │ block6c_dwconv2[… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_activation │ (None, 2, 2, │ 0 │ block6c_bn[0][0] │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_se_squeeze │ (None, 1152) │ 0 │ block6c_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_se_reshape │ (None, 1, 1, │ 0 │ block6c_se_squee… │ │ (Reshape) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block6c_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_se_expand │ (None, 1, 1, │ 56,448 │ block6c_se_reduc… │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_se_excite │ (None, 2, 2, │ 0 │ block6c_activati… │ │ (Multiply) │ 1152) │ │ block6c_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_project_co… │ (None, 2, 2, 192) │ 221,184 │ block6c_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_project_bn │ (None, 2, 2, 192) │ 768 │ block6c_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_drop │ (None, 2, 2, 192) │ 0 │ block6c_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6c_add (Add) │ (None, 2, 2, 192) │ 0 │ block6c_drop[0][… │ │ │ │ │ block6b_add[0][0] │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_expand_conv │ (None, 2, 2, │ 221,184 │ block6c_add[0][0] │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_expand_bn │ (None, 2, 2, │ 4,608 │ block6d_expand_c… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_expand_act… │ (None, 2, 2, │ 0 │ block6d_expand_b… │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_dwconv2 │ (None, 2, 2, │ 10,368 │ block6d_expand_a… │ │ (DepthwiseConv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_bn │ (None, 2, 2, │ 4,608 │ block6d_dwconv2[… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_activation │ (None, 2, 2, │ 0 │ block6d_bn[0][0] │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_se_squeeze │ (None, 1152) │ 0 │ block6d_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_se_reshape │ (None, 1, 1, │ 0 │ block6d_se_squee… │ │ (Reshape) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block6d_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_se_expand │ (None, 1, 1, │ 56,448 │ block6d_se_reduc… │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_se_excite │ (None, 2, 2, │ 0 │ block6d_activati… │ │ (Multiply) │ 1152) │ │ block6d_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_project_co… │ (None, 2, 2, 192) │ 221,184 │ block6d_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_project_bn │ (None, 2, 2, 192) │ 768 │ block6d_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_drop │ (None, 2, 2, 192) │ 0 │ block6d_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6d_add (Add) │ (None, 2, 2, 192) │ 0 │ block6d_drop[0][… │ │ │ │ │ block6c_add[0][0] │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_expand_conv │ (None, 2, 2, │ 221,184 │ block6d_add[0][0] │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_expand_bn │ (None, 2, 2, │ 4,608 │ block6e_expand_c… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_expand_act… │ (None, 2, 2, │ 0 │ block6e_expand_b… │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_dwconv2 │ (None, 2, 2, │ 10,368 │ block6e_expand_a… │ │ (DepthwiseConv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_bn │ (None, 2, 2, │ 4,608 │ block6e_dwconv2[… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_activation │ (None, 2, 2, │ 0 │ block6e_bn[0][0] │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_se_squeeze │ (None, 1152) │ 0 │ block6e_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_se_reshape │ (None, 1, 1, │ 0 │ block6e_se_squee… │ │ (Reshape) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block6e_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_se_expand │ (None, 1, 1, │ 56,448 │ block6e_se_reduc… │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_se_excite │ (None, 2, 2, │ 0 │ block6e_activati… │ │ (Multiply) │ 1152) │ │ block6e_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_project_co… │ (None, 2, 2, 192) │ 221,184 │ block6e_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_project_bn │ (None, 2, 2, 192) │ 768 │ block6e_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_drop │ (None, 2, 2, 192) │ 0 │ block6e_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6e_add (Add) │ (None, 2, 2, 192) │ 0 │ block6e_drop[0][… │ │ │ │ │ block6d_add[0][0] │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_expand_conv │ (None, 2, 2, │ 221,184 │ block6e_add[0][0] │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_expand_bn │ (None, 2, 2, │ 4,608 │ block6f_expand_c… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_expand_act… │ (None, 2, 2, │ 0 │ block6f_expand_b… │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_dwconv2 │ (None, 2, 2, │ 10,368 │ block6f_expand_a… │ │ (DepthwiseConv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_bn │ (None, 2, 2, │ 4,608 │ block6f_dwconv2[… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_activation │ (None, 2, 2, │ 0 │ block6f_bn[0][0] │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_se_squeeze │ (None, 1152) │ 0 │ block6f_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_se_reshape │ (None, 1, 1, │ 0 │ block6f_se_squee… │ │ (Reshape) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block6f_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_se_expand │ (None, 1, 1, │ 56,448 │ block6f_se_reduc… │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_se_excite │ (None, 2, 2, │ 0 │ block6f_activati… │ │ (Multiply) │ 1152) │ │ block6f_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_project_co… │ (None, 2, 2, 192) │ 221,184 │ block6f_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_project_bn │ (None, 2, 2, 192) │ 768 │ block6f_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_drop │ (None, 2, 2, 192) │ 0 │ block6f_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6f_add (Add) │ (None, 2, 2, 192) │ 0 │ block6f_drop[0][… │ │ │ │ │ block6e_add[0][0] │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_expand_conv │ (None, 2, 2, │ 221,184 │ block6f_add[0][0] │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_expand_bn │ (None, 2, 2, │ 4,608 │ block6g_expand_c… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_expand_act… │ (None, 2, 2, │ 0 │ block6g_expand_b… │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_dwconv2 │ (None, 2, 2, │ 10,368 │ block6g_expand_a… │ │ (DepthwiseConv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_bn │ (None, 2, 2, │ 4,608 │ block6g_dwconv2[… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_activation │ (None, 2, 2, │ 0 │ block6g_bn[0][0] │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_se_squeeze │ (None, 1152) │ 0 │ block6g_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_se_reshape │ (None, 1, 1, │ 0 │ block6g_se_squee… │ │ (Reshape) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block6g_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_se_expand │ (None, 1, 1, │ 56,448 │ block6g_se_reduc… │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_se_excite │ (None, 2, 2, │ 0 │ block6g_activati… │ │ (Multiply) │ 1152) │ │ block6g_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_project_co… │ (None, 2, 2, 192) │ 221,184 │ block6g_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_project_bn │ (None, 2, 2, 192) │ 768 │ block6g_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_drop │ (None, 2, 2, 192) │ 0 │ block6g_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6g_add (Add) │ (None, 2, 2, 192) │ 0 │ block6g_drop[0][… │ │ │ │ │ block6f_add[0][0] │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_expand_conv │ (None, 2, 2, │ 221,184 │ block6g_add[0][0] │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_expand_bn │ (None, 2, 2, │ 4,608 │ block6h_expand_c… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_expand_act… │ (None, 2, 2, │ 0 │ block6h_expand_b… │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_dwconv2 │ (None, 2, 2, │ 10,368 │ block6h_expand_a… │ │ (DepthwiseConv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_bn │ (None, 2, 2, │ 4,608 │ block6h_dwconv2[… │ │ (BatchNormalizatio… │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_activation │ (None, 2, 2, │ 0 │ block6h_bn[0][0] │ │ (Activation) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_se_squeeze │ (None, 1152) │ 0 │ block6h_activati… │ │ (GlobalAveragePool… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_se_reshape │ (None, 1, 1, │ 0 │ block6h_se_squee… │ │ (Reshape) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block6h_se_resha… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_se_expand │ (None, 1, 1, │ 56,448 │ block6h_se_reduc… │ │ (Conv2D) │ 1152) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_se_excite │ (None, 2, 2, │ 0 │ block6h_activati… │ │ (Multiply) │ 1152) │ │ block6h_se_expan… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_project_co… │ (None, 2, 2, 192) │ 221,184 │ block6h_se_excit… │ │ (Conv2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_project_bn │ (None, 2, 2, 192) │ 768 │ block6h_project_… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_drop │ (None, 2, 2, 192) │ 0 │ block6h_project_… │ │ (Dropout) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ block6h_add (Add) │ (None, 2, 2, 192) │ 0 │ block6h_drop[0][… │ │ │ │ │ block6g_add[0][0] │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ top_conv (Conv2D) │ (None, 2, 2, │ 245,760 │ block6h_add[0][0] │ │ │ 1280) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ top_bn │ (None, 2, 2, │ 5,120 │ top_conv[0][0] │ │ (BatchNormalizatio… │ 1280) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ top_activation │ (None, 2, 2, │ 0 │ top_bn[0][0] │ │ (Activation) │ 1280) │ │ │ └─────────────────────┴───────────────────┴────────────┴───────────────────┘
Total params: 5,919,312 (22.58 MB)
Trainable params: 0 (0.00 B)
Non-trainable params: 5,919,312 (22.58 MB)
Model Building¶
new_efficient_model = Sequential()
new_efficient_model.add(efficient_model)
# Reduces each feature map to a single value by averaging all elements
new_efficient_model.add(GlobalAveragePooling2D())
# Adding full connected layers
new_efficient_model.add(Dense(256, activation="relu"))
new_efficient_model.add(Dense(128, activation="relu"))
# Output Layer
new_efficient_model.add(Dense(4, activation="softmax"))
# Using Adam Optimizer
optimizer = Adam(learning_rate=0.01)
Compiling and Training the Model¶
new_efficient_model.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])
new_efficient_model.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ efficientnetv2-b0 (Functional) │ ? │ 5,919,312 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ global_average_pooling2d │ ? │ 0 (unbuilt) │ │ (GlobalAveragePooling2D) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense (Dense) │ ? │ 0 (unbuilt) │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_1 (Dense) │ ? │ 0 (unbuilt) │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_2 (Dense) │ ? │ 0 (unbuilt) │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 5,919,312 (22.58 MB)
Trainable params: 0 (0.00 B)
Non-trainable params: 5,919,312 (22.58 MB)
# Get the current time
current_time = datetime.now().strftime("%Y%m%d-%H%M%S")
# Set up Early Stopping with a patience 7 but acting after at least 20 epochs
delayed_early_stopping = DelayedEarlyStopping(
monitor="val_loss", patience=7, verbose=1, restore_best_weights=True, start_epoch=20
)
# Define the learning rate scheduler callback
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, patience=5, min_lr=0.00001, verbose=1)
# Define the saving the best model callback
mc = ModelCheckpoint(
f"{results_path}/best_model_efficient_{current_time}.keras",
monitor="val_accuracy",
mode="max",
verbose=1,
save_best_only=True,
)
# Fitting the model with 40 epochs and using validation set
history_efficient = new_efficient_model.fit(
train_generator_efficientnet,
epochs=40,
validation_data=validation_generator_efficientnet,
callbacks=[reduce_lr, mc, delayed_early_stopping],
)
Epoch 1/40
/home/iamtxena/sandbox/mit-ai/my_env/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored. self._warn_if_super_not_called()
I0000 00:00:1712795242.855338 1509123 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11837', 32 bytes spill stores, 32 bytes spill loads I0000 00:00:1712795242.912001 1509129 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11837', 40 bytes spill stores, 40 bytes spill loads
I0000 00:00:1712795243.210471 1509126 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11548', 32 bytes spill stores, 32 bytes spill loads I0000 00:00:1712795243.323623 1509129 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11837', 4 bytes spill stores, 4 bytes spill loads
I0000 00:00:1712795243.486678 1509130 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11858', 4 bytes spill stores, 4 bytes spill loads
I0000 00:00:1712795243.743913 1509132 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11548', 244 bytes spill stores, 244 bytes spill loads
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I0000 00:00:1712795261.341273 1509630 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11548', 20 bytes spill stores, 20 bytes spill loads
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I0000 00:00:1712795281.118442 1510282 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_2315', 260 bytes spill stores, 260 bytes spill loads
Epoch 1: val_accuracy improved from -inf to 0.55515, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 55s 69ms/step - accuracy: 0.4291 - loss: 1.3089 - val_accuracy: 0.5552 - val_loss: 1.0737 - learning_rate: 0.0100
Epoch 2/40
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Epoch 2: val_accuracy improved from 0.55515 to 0.56339, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5002 - loss: 1.1450 - val_accuracy: 0.5634 - val_loss: 1.0377 - learning_rate: 0.0100
Epoch 3/40
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Epoch 3: val_accuracy improved from 0.56339 to 0.56701, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5086 - loss: 1.1278 - val_accuracy: 0.5670 - val_loss: 1.0315 - learning_rate: 0.0100
Epoch 4/40
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319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5209 - loss: 1.1005
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5208 - loss: 1.1006
325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5207 - loss: 1.1008
328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5206 - loss: 1.1009
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5206 - loss: 1.1010
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5205 - loss: 1.1011
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5204 - loss: 1.1012
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5204 - loss: 1.1014
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5203 - loss: 1.1015
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5202 - loss: 1.1016
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5201 - loss: 1.1018
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5200 - loss: 1.1019
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5200 - loss: 1.1020
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5199 - loss: 1.1021
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5198 - loss: 1.1022
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5198 - loss: 1.1023
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5197 - loss: 1.1025
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5196 - loss: 1.1026
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5196 - loss: 1.1027
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5195 - loss: 1.1028
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5194 - loss: 1.1029
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5194 - loss: 1.1031
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5193 - loss: 1.1032
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5192 - loss: 1.1033
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5192 - loss: 1.1034
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5191 - loss: 1.1035
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5191 - loss: 1.1036
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5190 - loss: 1.1038
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5190 - loss: 1.1039
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5189 - loss: 1.1040
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5189 - loss: 1.1041
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5188 - loss: 1.1042
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5188 - loss: 1.1043
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5187 - loss: 1.1044
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5187 - loss: 1.1045
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5186 - loss: 1.1046
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5186 - loss: 1.1047
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5185 - loss: 1.1048
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5185 - loss: 1.1049
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5184 - loss: 1.1050
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5184 - loss: 1.1052
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5184 - loss: 1.1052
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5183 - loss: 1.1053
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5183 - loss: 1.1054
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5182 - loss: 1.1055
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5182 - loss: 1.1056
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5181 - loss: 1.1057
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5181 - loss: 1.1059
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5181 - loss: 1.1059
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5180 - loss: 1.1060
473/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5179 - loss: 1.1063
Epoch 4: val_accuracy did not improve from 0.56701
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5179 - loss: 1.1063 - val_accuracy: 0.5584 - val_loss: 1.0353 - learning_rate: 0.0100
Epoch 5/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 165ms/step - accuracy: 0.6250 - loss: 0.9951
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5807 - loss: 0.9968
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5690 - loss: 1.0114
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5570 - loss: 1.0343
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5476 - loss: 1.0543
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5409 - loss: 1.0699
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5370 - loss: 1.0826
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5338 - loss: 1.0913
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5302 - loss: 1.0983
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5276 - loss: 1.1039
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5255 - loss: 1.1087
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5237 - loss: 1.1124
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5224 - loss: 1.1156
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5216 - loss: 1.1178
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5209 - loss: 1.1195
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5206 - loss: 1.1206
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5205 - loss: 1.1211
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5207 - loss: 1.1211
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5210 - loss: 1.1209
58/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5213 - loss: 1.1207
61/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5214 - loss: 1.1204
64/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5216 - loss: 1.1202
67/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5217 - loss: 1.1201
70/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5217 - loss: 1.1202
73/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5217 - loss: 1.1204
75/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5216 - loss: 1.1206
77/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5214 - loss: 1.1207
80/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5211 - loss: 1.1211
83/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5208 - loss: 1.1216
86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5206 - loss: 1.1220
89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5203 - loss: 1.1224
92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5201 - loss: 1.1227
95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5198 - loss: 1.1231
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5196 - loss: 1.1235
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5194 - loss: 1.1240
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5191 - loss: 1.1244
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5189 - loss: 1.1248
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5187 - loss: 1.1253
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5185 - loss: 1.1256
116/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5182 - loss: 1.1259
119/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5179 - loss: 1.1263
122/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5176 - loss: 1.1266
125/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5174 - loss: 1.1269
128/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5171 - loss: 1.1272
131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5168 - loss: 1.1274
134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5166 - loss: 1.1276
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5164 - loss: 1.1278
140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5161 - loss: 1.1280
143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5160 - loss: 1.1282
146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5158 - loss: 1.1283
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5157 - loss: 1.1284
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5155 - loss: 1.1285
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5153 - loss: 1.1286
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5152 - loss: 1.1286
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5151 - loss: 1.1287
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5150 - loss: 1.1287
167/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5149 - loss: 1.1288
170/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5147 - loss: 1.1289
173/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5146 - loss: 1.1290
176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5144 - loss: 1.1291
179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5143 - loss: 1.1292
182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5141 - loss: 1.1293
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5140 - loss: 1.1293
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5139 - loss: 1.1293
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5138 - loss: 1.1294
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5137 - loss: 1.1294
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5136 - loss: 1.1295
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5135 - loss: 1.1296
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5134 - loss: 1.1296
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5133 - loss: 1.1296
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5132 - loss: 1.1296
212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5131 - loss: 1.1297
215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5131 - loss: 1.1297
218/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5130 - loss: 1.1297
221/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5130 - loss: 1.1297
224/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5129 - loss: 1.1296
227/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5129 - loss: 1.1296
230/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5128 - loss: 1.1296
233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5128 - loss: 1.1296
236/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5127 - loss: 1.1296
239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5127 - loss: 1.1296
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5126 - loss: 1.1296
244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5126 - loss: 1.1296
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5125 - loss: 1.1295
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5125 - loss: 1.1295
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5125 - loss: 1.1295
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5124 - loss: 1.1294
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5124 - loss: 1.1294
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5124 - loss: 1.1294
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5124 - loss: 1.1294
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5124 - loss: 1.1293
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5123 - loss: 1.1293
273/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5123 - loss: 1.1293
276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5123 - loss: 1.1292
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5123 - loss: 1.1292
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5123 - loss: 1.1292
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5122 - loss: 1.1292
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5122 - loss: 1.1291
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5122 - loss: 1.1291
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5122 - loss: 1.1291
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1290
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1290
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1289
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1288
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1288
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1287
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1287
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1286
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1286
324/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5121 - loss: 1.1285
327/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5121 - loss: 1.1285
330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5120 - loss: 1.1285
333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5120 - loss: 1.1284
336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5120 - loss: 1.1284
339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5120 - loss: 1.1283
342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5119 - loss: 1.1283
345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5119 - loss: 1.1283
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5119 - loss: 1.1282
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5119 - loss: 1.1282
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5118 - loss: 1.1282
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5118 - loss: 1.1282
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5118 - loss: 1.1282
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5117 - loss: 1.1282
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5117 - loss: 1.1281
369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5117 - loss: 1.1281
372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5116 - loss: 1.1281
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5116 - loss: 1.1281
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5116 - loss: 1.1281
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5115 - loss: 1.1281
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5115 - loss: 1.1281
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5115 - loss: 1.1281
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5114 - loss: 1.1281
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5114 - loss: 1.1281
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5114 - loss: 1.1281
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5114 - loss: 1.1281
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5113 - loss: 1.1280
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5113 - loss: 1.1280
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5113 - loss: 1.1280
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5113 - loss: 1.1280
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5113 - loss: 1.1279
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5112 - loss: 1.1279
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5112 - loss: 1.1279
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1278
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1278
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1278
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1277
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1277
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1276
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1276
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1275
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1275
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1274
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1274
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1273
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5113 - loss: 1.1273
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5113 - loss: 1.1272
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5113 - loss: 1.1272
Epoch 5: val_accuracy did not improve from 0.56701
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5113 - loss: 1.1270 - val_accuracy: 0.5612 - val_loss: 1.0790 - learning_rate: 0.0100
Epoch 6/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:26 183ms/step - accuracy: 0.5000 - loss: 0.9738
4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5228 - loss: 1.0491
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5348 - loss: 1.0663
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5303 - loss: 1.0826
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5297 - loss: 1.0888
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5307 - loss: 1.0917
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5329 - loss: 1.0905
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5353 - loss: 1.0895
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5361 - loss: 1.0910
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5369 - loss: 1.0918
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5376 - loss: 1.0915
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5383 - loss: 1.0910
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5391 - loss: 1.0906
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5392 - loss: 1.0906
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5392 - loss: 1.0905
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5391 - loss: 1.0905
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5388 - loss: 1.0906
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5384 - loss: 1.0909
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5380 - loss: 1.0912
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5375 - loss: 1.0914
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5373 - loss: 1.0916
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5370 - loss: 1.0919
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5367 - loss: 1.0921
70/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5367 - loss: 1.0920
73/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5366 - loss: 1.0920
76/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5365 - loss: 1.0919
79/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5365 - loss: 1.0918
82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5365 - loss: 1.0917
85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5365 - loss: 1.0916
88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5364 - loss: 1.0916
91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5363 - loss: 1.0917
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5362 - loss: 1.0917
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5362 - loss: 1.0918
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5360 - loss: 1.0919
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5360 - loss: 1.0918
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5360 - loss: 1.0918
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5359 - loss: 1.0920
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5357 - loss: 1.0921
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5355 - loss: 1.0923
118/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5353 - loss: 1.0924
121/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5351 - loss: 1.0926
124/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5349 - loss: 1.0927
127/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5347 - loss: 1.0930
130/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5344 - loss: 1.0933
133/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5341 - loss: 1.0935
135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5339 - loss: 1.0936
138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5337 - loss: 1.0939
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5335 - loss: 1.0940
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5333 - loss: 1.0943
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5331 - loss: 1.0945
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5329 - loss: 1.0947
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5326 - loss: 1.0949
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5325 - loss: 1.0951
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5321 - loss: 1.0953
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5319 - loss: 1.0956
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5317 - loss: 1.0958
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5315 - loss: 1.0960
171/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5313 - loss: 1.0962
174/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5311 - loss: 1.0963
177/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5308 - loss: 1.0965
180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5306 - loss: 1.0967
183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5303 - loss: 1.0969
186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5301 - loss: 1.0972
189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5299 - loss: 1.0974
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5297 - loss: 1.0976
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5294 - loss: 1.0978
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5292 - loss: 1.0981
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5290 - loss: 1.0983
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5287 - loss: 1.0984
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5285 - loss: 1.0986
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5283 - loss: 1.0988
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5281 - loss: 1.0989
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5279 - loss: 1.0991
219/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5277 - loss: 1.0993
222/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5275 - loss: 1.0994
225/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5273 - loss: 1.0996
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5271 - loss: 1.0997
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5270 - loss: 1.0998
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5268 - loss: 1.0999
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5267 - loss: 1.1001
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5265 - loss: 1.1002
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5264 - loss: 1.1004
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5262 - loss: 1.1006
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5261 - loss: 1.1007
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5260 - loss: 1.1009
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5258 - loss: 1.1010
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5257 - loss: 1.1012
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5255 - loss: 1.1014
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5254 - loss: 1.1016
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5253 - loss: 1.1017
270/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5251 - loss: 1.1019
272/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5250 - loss: 1.1020
275/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5249 - loss: 1.1022
278/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5248 - loss: 1.1023
281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5247 - loss: 1.1024
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5246 - loss: 1.1026
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5245 - loss: 1.1027
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5244 - loss: 1.1028
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5243 - loss: 1.1029
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5243 - loss: 1.1030
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5242 - loss: 1.1031
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5241 - loss: 1.1031
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5240 - loss: 1.1032
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5240 - loss: 1.1033
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5239 - loss: 1.1034
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5238 - loss: 1.1035
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5237 - loss: 1.1036
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5237 - loss: 1.1037
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5236 - loss: 1.1037
323/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5236 - loss: 1.1038
326/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5235 - loss: 1.1039
329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5235 - loss: 1.1039
332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5234 - loss: 1.1040
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5234 - loss: 1.1041
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5233 - loss: 1.1042
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5232 - loss: 1.1043
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5232 - loss: 1.1043
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5232 - loss: 1.1044
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5231 - loss: 1.1044
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5231 - loss: 1.1045
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5231 - loss: 1.1045
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5231 - loss: 1.1045
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5231 - loss: 1.1045
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5231 - loss: 1.1046
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5230 - loss: 1.1046
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5230 - loss: 1.1046
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1046
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1046
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1047
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1047
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1047
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1047
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1047
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5230 - loss: 1.1048
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5230 - loss: 1.1048
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5230 - loss: 1.1048
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5231 - loss: 1.1048
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5231 - loss: 1.1047
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5231 - loss: 1.1047
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5231 - loss: 1.1047
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5231 - loss: 1.1047
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5231 - loss: 1.1047
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5232 - loss: 1.1047
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5232 - loss: 1.1047
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5232 - loss: 1.1047
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5232 - loss: 1.1047
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5232 - loss: 1.1047
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5232 - loss: 1.1046
Epoch 6: val_accuracy did not improve from 0.56701
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5232 - loss: 1.1046 - val_accuracy: 0.5546 - val_loss: 1.0736 - learning_rate: 0.0100
Epoch 7/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 161ms/step - accuracy: 0.4062 - loss: 1.5171
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4889 - loss: 1.3080
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5020 - loss: 1.2414
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5070 - loss: 1.2142
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5116 - loss: 1.1969
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5150 - loss: 1.1841
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5171 - loss: 1.1746
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5185 - loss: 1.1658
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5189 - loss: 1.1596
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5185 - loss: 1.1549
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5189 - loss: 1.1506
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5194 - loss: 1.1469
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5199 - loss: 1.1435
39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5202 - loss: 1.1414
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5207 - loss: 1.1384
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5211 - loss: 1.1356
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5217 - loss: 1.1329
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5222 - loss: 1.1305
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5229 - loss: 1.1280
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5233 - loss: 1.1258
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5239 - loss: 1.1237
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5242 - loss: 1.1223
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5245 - loss: 1.1212
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5249 - loss: 1.1199
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5254 - loss: 1.1188
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5257 - loss: 1.1177
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5259 - loss: 1.1168
79/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5261 - loss: 1.1159
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5262 - loss: 1.1153
84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5263 - loss: 1.1145
87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5265 - loss: 1.1138
90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5266 - loss: 1.1130
93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5267 - loss: 1.1123
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5269 - loss: 1.1116
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5270 - loss: 1.1109
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5272 - loss: 1.1102
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5275 - loss: 1.1092
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5278 - loss: 1.1084
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5280 - loss: 1.1078
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5283 - loss: 1.1070
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5285 - loss: 1.1063
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5286 - loss: 1.1057
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5288 - loss: 1.1052
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5290 - loss: 1.1047
129/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5291 - loss: 1.1042
132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5292 - loss: 1.1038
135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5293 - loss: 1.1034
138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5295 - loss: 1.1029
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5296 - loss: 1.1026
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5297 - loss: 1.1022
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5298 - loss: 1.1019
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5299 - loss: 1.1015
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5300 - loss: 1.1013
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5300 - loss: 1.1010
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5301 - loss: 1.1007
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5302 - loss: 1.1005
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5304 - loss: 1.1003
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5304 - loss: 1.1001
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5306 - loss: 1.0999
174/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5306 - loss: 1.0996
177/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5307 - loss: 1.0995
180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5308 - loss: 1.0993
183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5309 - loss: 1.0991
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5309 - loss: 1.0990
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5310 - loss: 1.0988
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5311 - loss: 1.0986
193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5311 - loss: 1.0985
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5312 - loss: 1.0983
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5313 - loss: 1.0982
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5314 - loss: 1.0980
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5314 - loss: 1.0979
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5315 - loss: 1.0977
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5315 - loss: 1.0976
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5316 - loss: 1.0974
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5316 - loss: 1.0973
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5317 - loss: 1.0972
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5317 - loss: 1.0971
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5318 - loss: 1.0970
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5318 - loss: 1.0969
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5318 - loss: 1.0968
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5318 - loss: 1.0967
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5318 - loss: 1.0966
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5319 - loss: 1.0966
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5319 - loss: 1.0965
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5319 - loss: 1.0964
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5319 - loss: 1.0963
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5319 - loss: 1.0962
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5319 - loss: 1.0961
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5320 - loss: 1.0960
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5320 - loss: 1.0958
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5320 - loss: 1.0957
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5320 - loss: 1.0956
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5321 - loss: 1.0955
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5321 - loss: 1.0954
276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0953
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0952
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0952
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0951
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0950
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0950
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0949
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0948
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0948
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0947
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0946
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0945
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0945
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5322 - loss: 1.0944
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5322 - loss: 1.0943
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5322 - loss: 1.0942
324/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5322 - loss: 1.0941
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330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5323 - loss: 1.0940
333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5323 - loss: 1.0939
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342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5324 - loss: 1.0936
345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5324 - loss: 1.0935
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0934
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0933
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0932
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5326 - loss: 1.0932
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5326 - loss: 1.0931
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5326 - loss: 1.0930
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5326 - loss: 1.0930
369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5327 - loss: 1.0929
372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5327 - loss: 1.0928
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5327 - loss: 1.0928
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5327 - loss: 1.0927
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5327 - loss: 1.0927
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5328 - loss: 1.0926
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5328 - loss: 1.0926
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5328 - loss: 1.0925
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5328 - loss: 1.0925
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5328 - loss: 1.0924
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5328 - loss: 1.0924
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5328 - loss: 1.0923
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5329 - loss: 1.0923
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5329 - loss: 1.0922
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5329 - loss: 1.0922
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5329 - loss: 1.0922
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5329 - loss: 1.0921
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5329 - loss: 1.0921
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5329 - loss: 1.0920
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5329 - loss: 1.0920
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0920
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0919
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0919
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0918
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0918
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0918
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0918
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0918
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0917
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0917
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0917
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0917
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0916
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0916
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0916
Epoch 7: val_accuracy did not improve from 0.56701
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5329 - loss: 1.0916 - val_accuracy: 0.5124 - val_loss: 1.0865 - learning_rate: 0.0100
Epoch 8/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.5312 - loss: 1.1688
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5658 - loss: 1.0951
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5644 - loss: 1.0862
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5696 - loss: 1.0738
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5691 - loss: 1.0669
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5684 - loss: 1.0624
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5672 - loss: 1.0624
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5663 - loss: 1.0630
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5652 - loss: 1.0631
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5637 - loss: 1.0639
30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5625 - loss: 1.0645
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5605 - loss: 1.0659
36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5584 - loss: 1.0675
39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5563 - loss: 1.0692
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5541 - loss: 1.0708
44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5528 - loss: 1.0719
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5510 - loss: 1.0729
50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5493 - loss: 1.0740
53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5478 - loss: 1.0751
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5467 - loss: 1.0758
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5457 - loss: 1.0768
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5442 - loss: 1.0780
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5430 - loss: 1.0792
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5420 - loss: 1.0801
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5412 - loss: 1.0808
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5407 - loss: 1.0814
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5402 - loss: 1.0818
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5399 - loss: 1.0820
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5396 - loss: 1.0822
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5395 - loss: 1.0822
87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5394 - loss: 1.0823
90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5392 - loss: 1.0825
93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5391 - loss: 1.0826
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5389 - loss: 1.0827
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5388 - loss: 1.0828
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5387 - loss: 1.0828
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5386 - loss: 1.0829
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5385 - loss: 1.0829
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5385 - loss: 1.0829
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5384 - loss: 1.0829
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5384 - loss: 1.0829
119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5384 - loss: 1.0828
122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5383 - loss: 1.0828
125/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5383 - loss: 1.0829
128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5382 - loss: 1.0829
131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5382 - loss: 1.0830
134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5382 - loss: 1.0830
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5382 - loss: 1.0830
140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5383 - loss: 1.0830
142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5383 - loss: 1.0830
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5383 - loss: 1.0830
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0830
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0830
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0830
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0831
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0832
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0833
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0834
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5383 - loss: 1.0835
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5383 - loss: 1.0836
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5382 - loss: 1.0837
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5381 - loss: 1.0838
181/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5380 - loss: 1.0840
184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5379 - loss: 1.0841
187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5379 - loss: 1.0842
190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5378 - loss: 1.0843
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5377 - loss: 1.0843
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5376 - loss: 1.0844
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5376 - loss: 1.0845
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5375 - loss: 1.0845
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5374 - loss: 1.0846
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5372 - loss: 1.0847
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5371 - loss: 1.0848
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5370 - loss: 1.0850
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5369 - loss: 1.0850
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5368 - loss: 1.0851
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5367 - loss: 1.0852
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5366 - loss: 1.0853
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5365 - loss: 1.0854
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5364 - loss: 1.0855
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5363 - loss: 1.0856
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5362 - loss: 1.0857
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5361 - loss: 1.0858
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5360 - loss: 1.0858
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Epoch 8: val_accuracy improved from 0.56701 to 0.57103, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5324 - loss: 1.0863 - val_accuracy: 0.5710 - val_loss: 1.0222 - learning_rate: 0.0100
Epoch 9/40
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135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5383 - loss: 1.0813
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141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5382 - loss: 1.0815
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5382 - loss: 1.0816
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5382 - loss: 1.0817
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5382 - loss: 1.0817
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5382 - loss: 1.0818
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5381 - loss: 1.0818
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5381 - loss: 1.0818
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5381 - loss: 1.0818
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5380 - loss: 1.0819
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5380 - loss: 1.0819
170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5379 - loss: 1.0819
173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5378 - loss: 1.0819
176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5378 - loss: 1.0820
179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5377 - loss: 1.0820
182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5376 - loss: 1.0821
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5375 - loss: 1.0821
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5375 - loss: 1.0821
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5374 - loss: 1.0821
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5374 - loss: 1.0822
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5373 - loss: 1.0822
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5372 - loss: 1.0822
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5372 - loss: 1.0823
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5371 - loss: 1.0823
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5371 - loss: 1.0823
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5370 - loss: 1.0824
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5369 - loss: 1.0824
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5369 - loss: 1.0824
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5368 - loss: 1.0825
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5368 - loss: 1.0825
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5367 - loss: 1.0826
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5367 - loss: 1.0826
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5366 - loss: 1.0827
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5365 - loss: 1.0828
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5365 - loss: 1.0829
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5364 - loss: 1.0829
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5363 - loss: 1.0830
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5363 - loss: 1.0831
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5362 - loss: 1.0831
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5362 - loss: 1.0832
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5361 - loss: 1.0832
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5360 - loss: 1.0833
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5360 - loss: 1.0834
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5359 - loss: 1.0834
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5358 - loss: 1.0835
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5357 - loss: 1.0835
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5357 - loss: 1.0836
276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5356 - loss: 1.0836
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5356 - loss: 1.0837
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5355 - loss: 1.0837
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5355 - loss: 1.0837
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5354 - loss: 1.0838
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5353 - loss: 1.0839
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5353 - loss: 1.0839
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5352 - loss: 1.0840
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5352 - loss: 1.0840
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5351 - loss: 1.0841
305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5351 - loss: 1.0841
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5350 - loss: 1.0841
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5350 - loss: 1.0842
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5350 - loss: 1.0842
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5349 - loss: 1.0843
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5349 - loss: 1.0843
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5348 - loss: 1.0844
325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5348 - loss: 1.0844
328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5348 - loss: 1.0845
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5347 - loss: 1.0846
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5347 - loss: 1.0847
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5346 - loss: 1.0847
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5346 - loss: 1.0848
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5345 - loss: 1.0849
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5345 - loss: 1.0850
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5344 - loss: 1.0850
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5344 - loss: 1.0851
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5344 - loss: 1.0852
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5343 - loss: 1.0853
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5343 - loss: 1.0853
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5342 - loss: 1.0854
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5342 - loss: 1.0855
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5341 - loss: 1.0856
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5341 - loss: 1.0856
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5340 - loss: 1.0857
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5340 - loss: 1.0858
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5340 - loss: 1.0859
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5339 - loss: 1.0859
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5339 - loss: 1.0860
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5338 - loss: 1.0861
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5337 - loss: 1.0862
397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5337 - loss: 1.0863
400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5336 - loss: 1.0864
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5336 - loss: 1.0865
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5336 - loss: 1.0865
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5335 - loss: 1.0866
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5335 - loss: 1.0867
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5334 - loss: 1.0867
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5334 - loss: 1.0868
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5334 - loss: 1.0869
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5333 - loss: 1.0869
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5333 - loss: 1.0869
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5333 - loss: 1.0870
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5332 - loss: 1.0870
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5332 - loss: 1.0871
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5332 - loss: 1.0872
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5331 - loss: 1.0872
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5331 - loss: 1.0872
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5330 - loss: 1.0873
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5330 - loss: 1.0873
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5330 - loss: 1.0874
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0874
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0875
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0875
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5328 - loss: 1.0875
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5328 - loss: 1.0876
Epoch 9: val_accuracy did not improve from 0.57103
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5327 - loss: 1.0877 - val_accuracy: 0.5433 - val_loss: 1.0395 - learning_rate: 0.0100
Epoch 10/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:28 187ms/step - accuracy: 0.6562 - loss: 0.8568
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5859 - loss: 0.9892
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5549 - loss: 1.0283
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5516 - loss: 1.0330
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5511 - loss: 1.0351
15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5498 - loss: 1.0370
18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5478 - loss: 1.0400
21/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5478 - loss: 1.0395
24/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5479 - loss: 1.0395
27/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5471 - loss: 1.0398
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Epoch 10: val_accuracy improved from 0.57103 to 0.57344, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5375 - loss: 1.0648 - val_accuracy: 0.5734 - val_loss: 1.0266 - learning_rate: 0.0100
Epoch 11/40
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279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0862
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0861
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0861
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0861
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0860
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0860
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0860
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5321 - loss: 1.0860
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5322 - loss: 1.0859
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5322 - loss: 1.0859
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5322 - loss: 1.0858
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5322 - loss: 1.0858
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5323 - loss: 1.0858
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5323 - loss: 1.0857
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5323 - loss: 1.0857
322/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5324 - loss: 1.0856
325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5324 - loss: 1.0856
328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5324 - loss: 1.0855
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5324 - loss: 1.0855
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5324 - loss: 1.0854
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0854
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0853
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0853
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0853
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0852
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0852
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0852
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0851
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0851
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0851
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0850
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0850
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0850
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0850
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0849
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0849
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0849
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0849
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0848
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0848
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0848
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0848
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0848
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0847
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0847
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0847
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0847
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0846
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0846
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0846
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5324 - loss: 1.0845
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0845
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0845
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0844
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0844
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0843
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0843
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0843
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0842
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0842
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0841
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5326 - loss: 1.0841
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5326 - loss: 1.0840
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5326 - loss: 1.0840
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5326 - loss: 1.0839
Epoch 11: val_accuracy did not improve from 0.57344
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5326 - loss: 1.0839 - val_accuracy: 0.5620 - val_loss: 1.0391 - learning_rate: 0.0100
Epoch 12/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 160ms/step - accuracy: 0.3750 - loss: 1.1347
4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4284 - loss: 1.0906
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4428 - loss: 1.0865
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4526 - loss: 1.0836
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4626 - loss: 1.0785
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4729 - loss: 1.0710
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4797 - loss: 1.0666
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.4839 - loss: 1.0655
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.4873 - loss: 1.0639
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4893 - loss: 1.0625
30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.4921 - loss: 1.0618
33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.4950 - loss: 1.0609
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4967 - loss: 1.0604
38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.4986 - loss: 1.0604
41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5003 - loss: 1.0601
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5021 - loss: 1.0594
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5035 - loss: 1.0588
50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5046 - loss: 1.0589
53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5057 - loss: 1.0592
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5065 - loss: 1.0593
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5075 - loss: 1.0593
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5084 - loss: 1.0595
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5092 - loss: 1.0599
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5099 - loss: 1.0602
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5106 - loss: 1.0607
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5112 - loss: 1.0613
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5118 - loss: 1.0619
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5124 - loss: 1.0623
82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5131 - loss: 1.0627
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5135 - loss: 1.0630
87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5142 - loss: 1.0634
90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5147 - loss: 1.0639
93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5153 - loss: 1.0643
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5158 - loss: 1.0648
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5162 - loss: 1.0652
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5167 - loss: 1.0656
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5170 - loss: 1.0659
108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5174 - loss: 1.0662
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5177 - loss: 1.0666
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5179 - loss: 1.0669
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5182 - loss: 1.0672
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5184 - loss: 1.0674
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5186 - loss: 1.0676
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5189 - loss: 1.0677
128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5190 - loss: 1.0678
131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5192 - loss: 1.0680
134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5193 - loss: 1.0682
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5195 - loss: 1.0684
140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5196 - loss: 1.0687
143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5197 - loss: 1.0689
146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5199 - loss: 1.0691
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5199 - loss: 1.0694
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5200 - loss: 1.0697
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5201 - loss: 1.0700
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5201 - loss: 1.0702
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5202 - loss: 1.0705
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Epoch 12: val_accuracy improved from 0.57344 to 0.57685, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5243 - loss: 1.0817 - val_accuracy: 0.5769 - val_loss: 1.0251 - learning_rate: 0.0100
Epoch 13/40
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4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5840 - loss: 1.0620
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418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5461 - loss: 1.0766
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5460 - loss: 1.0766
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5460 - loss: 1.0766
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5460 - loss: 1.0766
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5460 - loss: 1.0766
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5460 - loss: 1.0766
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5459 - loss: 1.0766
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5459 - loss: 1.0766
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5459 - loss: 1.0766
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5459 - loss: 1.0766
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5459 - loss: 1.0765
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5458 - loss: 1.0765
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5458 - loss: 1.0765
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5458 - loss: 1.0765
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5458 - loss: 1.0764
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5458 - loss: 1.0764
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5458 - loss: 1.0764
Epoch 13: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.
Epoch 13: val_accuracy did not improve from 0.57685
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5457 - loss: 1.0763 - val_accuracy: 0.5668 - val_loss: 1.0359 - learning_rate: 0.0100
Epoch 14/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 161ms/step - accuracy: 0.4062 - loss: 1.3111
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4375 - loss: 1.2257
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.4524 - loss: 1.2047
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.4663 - loss: 1.1860
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4752 - loss: 1.1755
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.4832 - loss: 1.1660
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4891 - loss: 1.1590
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4940 - loss: 1.1518
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4978 - loss: 1.1452
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5012 - loss: 1.1388
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5039 - loss: 1.1337
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5059 - loss: 1.1300
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5077 - loss: 1.1266
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5093 - loss: 1.1234
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5106 - loss: 1.1208
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5118 - loss: 1.1188
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5126 - loss: 1.1175
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5132 - loss: 1.1164
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5138 - loss: 1.1155
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5144 - loss: 1.1147
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5150 - loss: 1.1139
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5155 - loss: 1.1130
67/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5159 - loss: 1.1123
70/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5163 - loss: 1.1117
73/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5167 - loss: 1.1111
76/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5171 - loss: 1.1104
79/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5176 - loss: 1.1097
82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5181 - loss: 1.1090
85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5185 - loss: 1.1083
88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5189 - loss: 1.1075
91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5194 - loss: 1.1067
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5199 - loss: 1.1059
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5204 - loss: 1.1051
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5207 - loss: 1.1046
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5211 - loss: 1.1041
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5216 - loss: 1.1034
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5221 - loss: 1.1028
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5226 - loss: 1.1021
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5231 - loss: 1.1014
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5235 - loss: 1.1008
119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5240 - loss: 1.1001
122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5245 - loss: 1.0995
125/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5249 - loss: 1.0989
128/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5254 - loss: 1.0982
131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5258 - loss: 1.0976
134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5263 - loss: 1.0969
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5267 - loss: 1.0963
140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5271 - loss: 1.0956
143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5275 - loss: 1.0950
146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5279 - loss: 1.0945
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5283 - loss: 1.0940
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5287 - loss: 1.0935
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5291 - loss: 1.0930
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5294 - loss: 1.0925
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5298 - loss: 1.0920
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5301 - loss: 1.0916
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5304 - loss: 1.0912
170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5307 - loss: 1.0908
173/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5310 - loss: 1.0904
176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5313 - loss: 1.0900
179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5316 - loss: 1.0896
182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5318 - loss: 1.0893
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5321 - loss: 1.0890
187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5322 - loss: 1.0887
190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5325 - loss: 1.0884
193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5327 - loss: 1.0880
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5330 - loss: 1.0877
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5332 - loss: 1.0873
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5335 - loss: 1.0870
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5337 - loss: 1.0866
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5339 - loss: 1.0863
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5341 - loss: 1.0859
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5344 - loss: 1.0856
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5346 - loss: 1.0852
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5348 - loss: 1.0849
223/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5351 - loss: 1.0846
226/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5353 - loss: 1.0842
229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5355 - loss: 1.0839
232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5357 - loss: 1.0836
235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5359 - loss: 1.0833
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5361 - loss: 1.0830
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5362 - loss: 1.0828
242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5363 - loss: 1.0826
244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5365 - loss: 1.0825
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5366 - loss: 1.0823
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5367 - loss: 1.0821
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5368 - loss: 1.0819
253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5370 - loss: 1.0817
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5372 - loss: 1.0814
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5374 - loss: 1.0811
262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5376 - loss: 1.0808
265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5377 - loss: 1.0806
268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5379 - loss: 1.0803
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5381 - loss: 1.0800
274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5382 - loss: 1.0798
277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5384 - loss: 1.0796
280/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5386 - loss: 1.0793
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5387 - loss: 1.0792
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5388 - loss: 1.0790
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5389 - loss: 1.0788
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5391 - loss: 1.0785
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5392 - loss: 1.0783
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5393 - loss: 1.0780
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Epoch 14: val_accuracy improved from 0.57685 to 0.58107, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5453 - loss: 1.0668 - val_accuracy: 0.5811 - val_loss: 0.9944 - learning_rate: 0.0020
Epoch 15/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 159ms/step - accuracy: 0.4375 - loss: 1.2007
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188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5612 - loss: 1.0185
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5611 - loss: 1.0186
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5610 - loss: 1.0187
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5610 - loss: 1.0188
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5609 - loss: 1.0189
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5608 - loss: 1.0190
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5608 - loss: 1.0191
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5607 - loss: 1.0192
212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5606 - loss: 1.0193
215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5605 - loss: 1.0194
218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5605 - loss: 1.0195
221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5604 - loss: 1.0196
224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5604 - loss: 1.0196
227/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5604 - loss: 1.0197
230/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0198
233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0198
236/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0198
239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0198
242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5602 - loss: 1.0198
245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5602 - loss: 1.0198
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5602 - loss: 1.0198
251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5602 - loss: 1.0198
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0198
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0198
260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0197
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0197
266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0196
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5604 - loss: 1.0196
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5604 - loss: 1.0196
275/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5604 - loss: 1.0195
278/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5604 - loss: 1.0195
281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5604 - loss: 1.0195
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5604 - loss: 1.0195
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5604 - loss: 1.0195
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194
302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0195
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5602 - loss: 1.0195
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5602 - loss: 1.0195
325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5602 - loss: 1.0196
328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5602 - loss: 1.0196
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5601 - loss: 1.0196
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5601 - loss: 1.0197
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5601 - loss: 1.0197
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5600 - loss: 1.0198
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5600 - loss: 1.0198
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5600 - loss: 1.0198
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5600 - loss: 1.0199
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0199
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0200
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0200
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0201
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0201
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5598 - loss: 1.0202
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5598 - loss: 1.0202
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5598 - loss: 1.0203
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5597 - loss: 1.0203
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5597 - loss: 1.0204
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5597 - loss: 1.0204
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5597 - loss: 1.0205
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5596 - loss: 1.0205
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5596 - loss: 1.0206
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5596 - loss: 1.0206
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5596 - loss: 1.0207
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5595 - loss: 1.0207
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5595 - loss: 1.0208
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5595 - loss: 1.0208
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5595 - loss: 1.0209
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5594 - loss: 1.0209
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5594 - loss: 1.0210
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5594 - loss: 1.0210
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5594 - loss: 1.0211
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5594 - loss: 1.0211
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5593 - loss: 1.0212
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5593 - loss: 1.0212
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5593 - loss: 1.0213
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5593 - loss: 1.0213
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5593 - loss: 1.0214
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5593 - loss: 1.0214
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0215
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0215
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0216
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0216
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0217
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0217
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0218
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0218
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0219
Epoch 15: val_accuracy did not improve from 0.58107
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5591 - loss: 1.0220 - val_accuracy: 0.5773 - val_loss: 0.9963 - learning_rate: 0.0020
Epoch 16/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 166ms/step - accuracy: 0.5000 - loss: 1.1545
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5013 - loss: 1.0755
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5167 - loss: 1.0549
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5265 - loss: 1.0448
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5331 - loss: 1.0373
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5363 - loss: 1.0355
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5380 - loss: 1.0355
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5402 - loss: 1.0352
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5422 - loss: 1.0343
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5430 - loss: 1.0345
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5433 - loss: 1.0344
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5434 - loss: 1.0340
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5437 - loss: 1.0334
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5440 - loss: 1.0330
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5442 - loss: 1.0328
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5443 - loss: 1.0331
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5445 - loss: 1.0333
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5447 - loss: 1.0334
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5451 - loss: 1.0336
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5454 - loss: 1.0337
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5457 - loss: 1.0337
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5458 - loss: 1.0340
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5459 - loss: 1.0343
68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5460 - loss: 1.0345
71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5461 - loss: 1.0347
74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5462 - loss: 1.0350
77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5464 - loss: 1.0352
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Epoch 16: val_accuracy improved from 0.58107 to 0.58449, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5574 - loss: 1.0329 - val_accuracy: 0.5845 - val_loss: 0.9837 - learning_rate: 0.0020
Epoch 17/40
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Epoch 17: val_accuracy improved from 0.58449 to 0.58590, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5627 - loss: 1.0313 - val_accuracy: 0.5859 - val_loss: 0.9805 - learning_rate: 0.0020
Epoch 18/40
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Epoch 18: val_accuracy improved from 0.58590 to 0.58991, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5648 - loss: 1.0168 - val_accuracy: 0.5899 - val_loss: 0.9764 - learning_rate: 0.0020
Epoch 19/40
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443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5670 - loss: 1.0225
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449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5669 - loss: 1.0225
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5669 - loss: 1.0226
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5669 - loss: 1.0226
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5668 - loss: 1.0226
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5668 - loss: 1.0227
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5668 - loss: 1.0227
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5667 - loss: 1.0228
Epoch 19: val_accuracy did not improve from 0.58991
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5666 - loss: 1.0228 - val_accuracy: 0.5811 - val_loss: 0.9902 - learning_rate: 0.0020
Epoch 20/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 161ms/step - accuracy: 0.4688 - loss: 1.0892
4/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.4674 - loss: 1.1093
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4852 - loss: 1.0993
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4878 - loss: 1.0971
12/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.4934 - loss: 1.0912
15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4988 - loss: 1.0879
17/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5017 - loss: 1.0868
20/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5055 - loss: 1.0840
23/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5102 - loss: 1.0794
26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5141 - loss: 1.0751
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5178 - loss: 1.0707
32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5212 - loss: 1.0663
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5240 - loss: 1.0627
38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5268 - loss: 1.0597
41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5290 - loss: 1.0576
44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5309 - loss: 1.0562
47/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5326 - loss: 1.0548
50/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5341 - loss: 1.0537
52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5351 - loss: 1.0529
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5363 - loss: 1.0520
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5374 - loss: 1.0512
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5384 - loss: 1.0503
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5394 - loss: 1.0496
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5399 - loss: 1.0491
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5406 - loss: 1.0484
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5413 - loss: 1.0477
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5419 - loss: 1.0470
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5426 - loss: 1.0463
81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5432 - loss: 1.0457
84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5438 - loss: 1.0454
86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5441 - loss: 1.0452
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5444 - loss: 1.0451
91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5448 - loss: 1.0449
94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5451 - loss: 1.0449
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5453 - loss: 1.0449
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5456 - loss: 1.0450
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5458 - loss: 1.0450
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5461 - loss: 1.0450
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5464 - loss: 1.0450
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5466 - loss: 1.0450
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5468 - loss: 1.0450
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5469 - loss: 1.0450
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5470 - loss: 1.0450
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5472 - loss: 1.0449
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5473 - loss: 1.0449
129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5474 - loss: 1.0448
132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5476 - loss: 1.0447
135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5478 - loss: 1.0445
138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5479 - loss: 1.0444
140/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5480 - loss: 1.0443
143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5482 - loss: 1.0442
146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5484 - loss: 1.0440
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5485 - loss: 1.0438
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5486 - loss: 1.0436
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5487 - loss: 1.0435
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5489 - loss: 1.0434
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5489 - loss: 1.0433
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5490 - loss: 1.0432
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5491 - loss: 1.0430
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5491 - loss: 1.0429
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5492 - loss: 1.0428
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5493 - loss: 1.0427
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5494 - loss: 1.0425
180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5494 - loss: 1.0424
183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5495 - loss: 1.0423
186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5495 - loss: 1.0421
189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5496 - loss: 1.0420
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5496 - loss: 1.0419
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5497 - loss: 1.0418
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5498 - loss: 1.0417
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5498 - loss: 1.0416
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5499 - loss: 1.0415
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5500 - loss: 1.0414
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5501 - loss: 1.0413
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5502 - loss: 1.0412
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5503 - loss: 1.0411
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5503 - loss: 1.0410
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5504 - loss: 1.0409
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5505 - loss: 1.0409
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5505 - loss: 1.0408
231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5506 - loss: 1.0408
233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5506 - loss: 1.0407
236/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5507 - loss: 1.0407
239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5507 - loss: 1.0406
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5508 - loss: 1.0406
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5508 - loss: 1.0406
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5508 - loss: 1.0405
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5509 - loss: 1.0405
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5510 - loss: 1.0404
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5510 - loss: 1.0403
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5511 - loss: 1.0402
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5512 - loss: 1.0401
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5513 - loss: 1.0400
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5514 - loss: 1.0399
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5515 - loss: 1.0398
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5516 - loss: 1.0397
276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5517 - loss: 1.0396
279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5518 - loss: 1.0395
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5519 - loss: 1.0394
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5519 - loss: 1.0393
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5520 - loss: 1.0391
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5521 - loss: 1.0390
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5522 - loss: 1.0389
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5523 - loss: 1.0388
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5524 - loss: 1.0387
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5524 - loss: 1.0385
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5525 - loss: 1.0384
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5526 - loss: 1.0383
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5526 - loss: 1.0382
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5527 - loss: 1.0381
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5527 - loss: 1.0380
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5528 - loss: 1.0379
324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5529 - loss: 1.0378
327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5529 - loss: 1.0378
330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5530 - loss: 1.0377
332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5530 - loss: 1.0376
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5530 - loss: 1.0376
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5531 - loss: 1.0375
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5531 - loss: 1.0374
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5532 - loss: 1.0373
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5532 - loss: 1.0372
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5532 - loss: 1.0372
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5533 - loss: 1.0371
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5533 - loss: 1.0371
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5534 - loss: 1.0370
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5534 - loss: 1.0369
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5534 - loss: 1.0369
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5535 - loss: 1.0368
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5535 - loss: 1.0368
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5535 - loss: 1.0367
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5536 - loss: 1.0367
376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5536 - loss: 1.0366
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5537 - loss: 1.0366
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5537 - loss: 1.0365
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5537 - loss: 1.0365
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5538 - loss: 1.0364
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5538 - loss: 1.0364
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5539 - loss: 1.0363
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5539 - loss: 1.0363
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5539 - loss: 1.0363
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5539 - loss: 1.0362
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5540 - loss: 1.0362
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5540 - loss: 1.0361
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5540 - loss: 1.0361
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5541 - loss: 1.0360
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5541 - loss: 1.0360
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5541 - loss: 1.0359
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5542 - loss: 1.0359
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5542 - loss: 1.0358
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5543 - loss: 1.0358
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5543 - loss: 1.0357
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5543 - loss: 1.0356
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5544 - loss: 1.0356
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5544 - loss: 1.0355
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5545 - loss: 1.0355
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5545 - loss: 1.0354
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0353
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0353
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0352
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5547 - loss: 1.0352
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5547 - loss: 1.0351
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5547 - loss: 1.0351
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5548 - loss: 1.0350
Epoch 20: val_accuracy did not improve from 0.58991
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5548 - loss: 1.0350 - val_accuracy: 0.5897 - val_loss: 0.9865 - learning_rate: 0.0020
Epoch 21/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 160ms/step - accuracy: 0.5938 - loss: 0.9371
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5807 - loss: 0.9520
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5570 - loss: 0.9910
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5446 - loss: 1.0062
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5385 - loss: 1.0189
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5353 - loss: 1.0264
18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5350 - loss: 1.0288
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5353 - loss: 1.0295
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5350 - loss: 1.0303
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5352 - loss: 1.0311
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5360 - loss: 1.0307
33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5366 - loss: 1.0302
35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5376 - loss: 1.0294
38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5387 - loss: 1.0285
41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5398 - loss: 1.0280
44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5409 - loss: 1.0274
47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5424 - loss: 1.0265
50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5440 - loss: 1.0253
53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5454 - loss: 1.0242
56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5469 - loss: 1.0229
59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5483 - loss: 1.0219
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5495 - loss: 1.0210
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5506 - loss: 1.0201
68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5516 - loss: 1.0192
71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5526 - loss: 1.0184
74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5535 - loss: 1.0176
77/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5544 - loss: 1.0167
80/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5552 - loss: 1.0160
83/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5559 - loss: 1.0155
86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5566 - loss: 1.0151
89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5572 - loss: 1.0147
92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5578 - loss: 1.0145
95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5585 - loss: 1.0143
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5590 - loss: 1.0141
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5594 - loss: 1.0141
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5598 - loss: 1.0140
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5602 - loss: 1.0139
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5606 - loss: 1.0138
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5610 - loss: 1.0137
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5613 - loss: 1.0136
119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5616 - loss: 1.0136
122/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0136
125/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5620 - loss: 1.0136
128/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5621 - loss: 1.0138
131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5623 - loss: 1.0138
134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5625 - loss: 1.0139
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5626 - loss: 1.0140
140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5628 - loss: 1.0141
143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5629 - loss: 1.0142
146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5631 - loss: 1.0142
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5632 - loss: 1.0144
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5633 - loss: 1.0145
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5635 - loss: 1.0146
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5636 - loss: 1.0147
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0148
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5638 - loss: 1.0149
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5639 - loss: 1.0151
170/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5640 - loss: 1.0152
173/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5641 - loss: 1.0153
176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5642 - loss: 1.0154
179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5643 - loss: 1.0156
182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5644 - loss: 1.0157
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5644 - loss: 1.0159
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5645 - loss: 1.0160
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5645 - loss: 1.0161
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5645 - loss: 1.0163
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5646 - loss: 1.0164
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0165
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0166
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0167
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5648 - loss: 1.0168
212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5648 - loss: 1.0169
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Epoch 21: val_accuracy improved from 0.58991 to 0.59011, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5666 - loss: 1.0202 - val_accuracy: 0.5901 - val_loss: 0.9813 - learning_rate: 0.0020
Epoch 22/40
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108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5567 - loss: 1.0499
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5566 - loss: 1.0497
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5565 - loss: 1.0494
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5564 - loss: 1.0492
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5563 - loss: 1.0490
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5562 - loss: 1.0487
125/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5562 - loss: 1.0486
128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5561 - loss: 1.0485
131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5561 - loss: 1.0483
134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5560 - loss: 1.0482
137/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5560 - loss: 1.0480
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142/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5559 - loss: 1.0477
145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5559 - loss: 1.0476
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5559 - loss: 1.0474
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5558 - loss: 1.0472
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5558 - loss: 1.0470
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159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5558 - loss: 1.0466
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5557 - loss: 1.0465
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173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5557 - loss: 1.0459
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5557 - loss: 1.0457
179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5557 - loss: 1.0455
181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5557 - loss: 1.0454
183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5558 - loss: 1.0452
186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5558 - loss: 1.0450
189/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5559 - loss: 1.0447
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5559 - loss: 1.0445
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5560 - loss: 1.0442
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5560 - loss: 1.0440
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5561 - loss: 1.0438
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207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5561 - loss: 1.0435
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5562 - loss: 1.0433
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5562 - loss: 1.0430
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5563 - loss: 1.0428
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5564 - loss: 1.0426
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5564 - loss: 1.0424
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5565 - loss: 1.0421
228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5566 - loss: 1.0419
231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5566 - loss: 1.0417
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5567 - loss: 1.0414
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5568 - loss: 1.0412
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5568 - loss: 1.0409
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5569 - loss: 1.0407
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5570 - loss: 1.0404
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5570 - loss: 1.0402
251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5571 - loss: 1.0400
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5571 - loss: 1.0398
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5572 - loss: 1.0396
260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5572 - loss: 1.0393
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5573 - loss: 1.0391
266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5573 - loss: 1.0389
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5574 - loss: 1.0387
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5574 - loss: 1.0385
275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5574 - loss: 1.0384
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5575 - loss: 1.0382
281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5575 - loss: 1.0380
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5576 - loss: 1.0379
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5576 - loss: 1.0377
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5577 - loss: 1.0376
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5577 - loss: 1.0374
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5578 - loss: 1.0373
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5578 - loss: 1.0371
302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5578 - loss: 1.0370
305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5579 - loss: 1.0369
308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5579 - loss: 1.0368
311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5579 - loss: 1.0367
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5580 - loss: 1.0365
317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5580 - loss: 1.0364
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5580 - loss: 1.0363
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5581 - loss: 1.0362
326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5581 - loss: 1.0361
329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5581 - loss: 1.0360
332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5582 - loss: 1.0359
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5582 - loss: 1.0358
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5582 - loss: 1.0357
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5582 - loss: 1.0356
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5583 - loss: 1.0355
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5583 - loss: 1.0354
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5583 - loss: 1.0353
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5584 - loss: 1.0352
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5584 - loss: 1.0351
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5584 - loss: 1.0350
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5585 - loss: 1.0349
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5585 - loss: 1.0348
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5585 - loss: 1.0347
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5586 - loss: 1.0346
374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5586 - loss: 1.0345
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5586 - loss: 1.0344
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5587 - loss: 1.0344
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5587 - loss: 1.0343
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5587 - loss: 1.0342
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5587 - loss: 1.0342
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5587 - loss: 1.0341
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5587 - loss: 1.0340
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5588 - loss: 1.0340
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5588 - loss: 1.0339
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5588 - loss: 1.0339
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5588 - loss: 1.0338
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5588 - loss: 1.0337
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5588 - loss: 1.0337
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5589 - loss: 1.0336
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5589 - loss: 1.0335
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5589 - loss: 1.0334
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5589 - loss: 1.0334
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5589 - loss: 1.0333
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5590 - loss: 1.0332
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5590 - loss: 1.0331
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5590 - loss: 1.0330
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5590 - loss: 1.0329
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0328
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0327
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0327
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0326
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0325
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0324
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0323
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0323
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0322
Epoch 22: val_accuracy did not improve from 0.59011
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5593 - loss: 1.0319 - val_accuracy: 0.5881 - val_loss: 0.9844 - learning_rate: 0.0020
Epoch 23/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.6562 - loss: 0.8490
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.6204 - loss: 0.8606
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6149 - loss: 0.8746
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6133 - loss: 0.8882
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6104 - loss: 0.9031
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6070 - loss: 0.9169
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6054 - loss: 0.9257
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6038 - loss: 0.9326
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6022 - loss: 0.9379
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6009 - loss: 0.9416
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5995 - loss: 0.9455
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5981 - loss: 0.9488
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5965 - loss: 0.9517
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5959 - loss: 0.9535
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5955 - loss: 0.9550
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5950 - loss: 0.9566
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5946 - loss: 0.9582
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5942 - loss: 0.9595
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5937 - loss: 0.9608
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5930 - loss: 0.9623
61/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5924 - loss: 0.9637
64/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5920 - loss: 0.9649
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5918 - loss: 0.9656
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5915 - loss: 0.9667
72/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5911 - loss: 0.9679
75/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5908 - loss: 0.9690
78/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5905 - loss: 0.9700
81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5902 - loss: 0.9709
84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5900 - loss: 0.9719
87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5896 - loss: 0.9729
90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5893 - loss: 0.9737
93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5890 - loss: 0.9745
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5887 - loss: 0.9752
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5884 - loss: 0.9759
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5881 - loss: 0.9766
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5878 - loss: 0.9774
108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5875 - loss: 0.9782
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5872 - loss: 0.9789
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5870 - loss: 0.9796
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5869 - loss: 0.9800
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5867 - loss: 0.9804
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5864 - loss: 0.9811
124/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5861 - loss: 0.9817
127/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5858 - loss: 0.9823
130/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5855 - loss: 0.9829
133/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5852 - loss: 0.9835
136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5849 - loss: 0.9842
139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5846 - loss: 0.9848
142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5843 - loss: 0.9854
145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5841 - loss: 0.9859
148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5838 - loss: 0.9864
151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5837 - loss: 0.9869
154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5835 - loss: 0.9874
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5833 - loss: 0.9878
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5831 - loss: 0.9883
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5829 - loss: 0.9887
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5828 - loss: 0.9891
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5826 - loss: 0.9895
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5825 - loss: 0.9899
175/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5823 - loss: 0.9904
178/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5822 - loss: 0.9908
181/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5820 - loss: 0.9913
184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5818 - loss: 0.9918
187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5816 - loss: 0.9922
190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5815 - loss: 0.9926
193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5813 - loss: 0.9930
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5811 - loss: 0.9934
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5809 - loss: 0.9938
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5807 - loss: 0.9942
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5805 - loss: 0.9946
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5803 - loss: 0.9949
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5801 - loss: 0.9953
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5800 - loss: 0.9956
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5798 - loss: 0.9959
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5796 - loss: 0.9962
223/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5794 - loss: 0.9965
226/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5792 - loss: 0.9969
229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5791 - loss: 0.9972
232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5789 - loss: 0.9975
235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5787 - loss: 0.9977
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5786 - loss: 0.9980
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5784 - loss: 0.9983
244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5783 - loss: 0.9986
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5782 - loss: 0.9988
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5780 - loss: 0.9991
253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5779 - loss: 0.9993
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5778 - loss: 0.9996
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5776 - loss: 0.9998
262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5775 - loss: 1.0000
265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5774 - loss: 1.0002
268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5773 - loss: 1.0004
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5772 - loss: 1.0006
274/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5771 - loss: 1.0008
277/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5770 - loss: 1.0010
280/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5769 - loss: 1.0012
283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5769 - loss: 1.0013
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5768 - loss: 1.0015
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5767 - loss: 1.0016
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5766 - loss: 1.0018
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5765 - loss: 1.0020
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5764 - loss: 1.0021
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5763 - loss: 1.0023
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5762 - loss: 1.0024
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5762 - loss: 1.0025
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5761 - loss: 1.0027
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5760 - loss: 1.0028
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5759 - loss: 1.0029
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5759 - loss: 1.0031
322/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5758 - loss: 1.0032
325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5757 - loss: 1.0033
328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5756 - loss: 1.0034
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5755 - loss: 1.0036
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5755 - loss: 1.0037
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5753 - loss: 1.0038
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5753 - loss: 1.0040
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5752 - loss: 1.0041
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5751 - loss: 1.0042
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5750 - loss: 1.0043
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5750 - loss: 1.0045
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 1.0046
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5748 - loss: 1.0047
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5747 - loss: 1.0048
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5747 - loss: 1.0049
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5746 - loss: 1.0050
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5745 - loss: 1.0051
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5745 - loss: 1.0052
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5744 - loss: 1.0054
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5743 - loss: 1.0055
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5742 - loss: 1.0056
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5741 - loss: 1.0057
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5741 - loss: 1.0059
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5740 - loss: 1.0060
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5739 - loss: 1.0061
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5739 - loss: 1.0062
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5738 - loss: 1.0063
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5737 - loss: 1.0064
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5736 - loss: 1.0065
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5736 - loss: 1.0067
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5735 - loss: 1.0068
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5734 - loss: 1.0069
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5734 - loss: 1.0070
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5733 - loss: 1.0071
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5732 - loss: 1.0072
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5731 - loss: 1.0074
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5731 - loss: 1.0075
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5730 - loss: 1.0076
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5729 - loss: 1.0077
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5729 - loss: 1.0078
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5728 - loss: 1.0080
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5727 - loss: 1.0081
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0082
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0083
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0084
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5724 - loss: 1.0085
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5724 - loss: 1.0086
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5723 - loss: 1.0088
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5722 - loss: 1.0089
Epoch 23: ReduceLROnPlateau reducing learning rate to 0.0003999999724328518.
Epoch 23: val_accuracy did not improve from 0.59011
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5721 - loss: 1.0091 - val_accuracy: 0.5887 - val_loss: 0.9781 - learning_rate: 0.0020
Epoch 24/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 160ms/step - accuracy: 0.5625 - loss: 1.0324
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5436 - loss: 1.0611
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5478 - loss: 1.0466
9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5470 - loss: 1.0453
12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5496 - loss: 1.0433
15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5497 - loss: 1.0425
18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5491 - loss: 1.0430
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5481 - loss: 1.0434
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5470 - loss: 1.0444
26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5465 - loss: 1.0447
29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5459 - loss: 1.0456
31/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5454 - loss: 1.0464
34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5453 - loss: 1.0473
37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5452 - loss: 1.0483
40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5451 - loss: 1.0494
43/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5452 - loss: 1.0499
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5456 - loss: 1.0498
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5459 - loss: 1.0496
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5461 - loss: 1.0496
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5464 - loss: 1.0495
56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5467 - loss: 1.0493
59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5470 - loss: 1.0491
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5471 - loss: 1.0490
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5474 - loss: 1.0486
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5475 - loss: 1.0484
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5477 - loss: 1.0482
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5479 - loss: 1.0478
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5481 - loss: 1.0476
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5484 - loss: 1.0472
80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5485 - loss: 1.0469
83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5487 - loss: 1.0466
86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5488 - loss: 1.0462
89/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5490 - loss: 1.0460
92/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5492 - loss: 1.0457
95/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5494 - loss: 1.0454
98/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5495 - loss: 1.0452
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5496 - loss: 1.0450
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5497 - loss: 1.0448
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5498 - loss: 1.0445
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5500 - loss: 1.0443
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5501 - loss: 1.0441
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5502 - loss: 1.0439
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5503 - loss: 1.0438
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5504 - loss: 1.0437
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5505 - loss: 1.0436
127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5506 - loss: 1.0436
129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5506 - loss: 1.0436
132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5507 - loss: 1.0435
134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5508 - loss: 1.0435
136/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5508 - loss: 1.0435
139/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5508 - loss: 1.0435
142/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5509 - loss: 1.0434
145/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5510 - loss: 1.0433
148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5511 - loss: 1.0432
151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5512 - loss: 1.0430
154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5514 - loss: 1.0429
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5515 - loss: 1.0427
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5517 - loss: 1.0425
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5519 - loss: 1.0423
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5520 - loss: 1.0422
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5521 - loss: 1.0420
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5522 - loss: 1.0418
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5523 - loss: 1.0416
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5525 - loss: 1.0415
180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5526 - loss: 1.0413
183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5528 - loss: 1.0410
185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5528 - loss: 1.0409
187/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5529 - loss: 1.0408
190/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5530 - loss: 1.0406
193/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5532 - loss: 1.0404
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5533 - loss: 1.0403
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5534 - loss: 1.0401
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5534 - loss: 1.0400
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5535 - loss: 1.0398
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5536 - loss: 1.0397
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5537 - loss: 1.0396
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5538 - loss: 1.0395
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5539 - loss: 1.0393
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5540 - loss: 1.0392
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5540 - loss: 1.0391
224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5541 - loss: 1.0390
227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5542 - loss: 1.0389
230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5543 - loss: 1.0388
233/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5544 - loss: 1.0386
236/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5545 - loss: 1.0385
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Epoch 24: val_accuracy improved from 0.59011 to 0.59052, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5575 - loss: 1.0339 - val_accuracy: 0.5905 - val_loss: 0.9716 - learning_rate: 4.0000e-04
Epoch 25/40
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Epoch 25: val_accuracy improved from 0.59052 to 0.59072, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5713 - loss: 1.0176 - val_accuracy: 0.5907 - val_loss: 0.9717 - learning_rate: 4.0000e-04
Epoch 26/40
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4/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5957 - loss: 1.0081
6/473 ━━━━━━━━━━━━━━━━━━━━ 11s 26ms/step - accuracy: 0.5947 - loss: 1.0016
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369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5714 - loss: 1.0162
372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5713 - loss: 1.0162
375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5713 - loss: 1.0162
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0162
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0162
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5712 - loss: 1.0162
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0163
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0163
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5711 - loss: 1.0163
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5711 - loss: 1.0163
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5711 - loss: 1.0163
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5710 - loss: 1.0163
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5710 - loss: 1.0163
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5710 - loss: 1.0163
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5710 - loss: 1.0163
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5709 - loss: 1.0164
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5709 - loss: 1.0164
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5709 - loss: 1.0164
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5709 - loss: 1.0164
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5708 - loss: 1.0164
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5708 - loss: 1.0164
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5708 - loss: 1.0164
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5708 - loss: 1.0164
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5708 - loss: 1.0164
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5707 - loss: 1.0164
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5707 - loss: 1.0164
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5707 - loss: 1.0164
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5707 - loss: 1.0164
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5706 - loss: 1.0165
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5706 - loss: 1.0165
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5706 - loss: 1.0165
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5705 - loss: 1.0165
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5705 - loss: 1.0165
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5705 - loss: 1.0165
Epoch 26: val_accuracy did not improve from 0.59072
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5704 - loss: 1.0166 - val_accuracy: 0.5895 - val_loss: 0.9729 - learning_rate: 4.0000e-04
Epoch 27/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 162ms/step - accuracy: 0.5938 - loss: 1.0087
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6302 - loss: 0.9698
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6390 - loss: 0.9481
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.6337 - loss: 0.9477
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6335 - loss: 0.9418
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6315 - loss: 0.9424
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6279 - loss: 0.9459
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6247 - loss: 0.9489
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6215 - loss: 0.9527
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6192 - loss: 0.9554
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6172 - loss: 0.9581
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6156 - loss: 0.9605
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6134 - loss: 0.9633
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6112 - loss: 0.9662
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6092 - loss: 0.9690
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6076 - loss: 0.9711
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6058 - loss: 0.9734
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6047 - loss: 0.9750
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6033 - loss: 0.9770
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6019 - loss: 0.9788
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6007 - loss: 0.9803
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5997 - loss: 0.9815
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5988 - loss: 0.9827
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5978 - loss: 0.9839
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5968 - loss: 0.9850
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5958 - loss: 0.9862
77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5951 - loss: 0.9869
80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5942 - loss: 0.9881
83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5933 - loss: 0.9891
86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5924 - loss: 0.9901
89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5916 - loss: 0.9910
92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5908 - loss: 0.9921
95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5901 - loss: 0.9930
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5894 - loss: 0.9939
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5888 - loss: 0.9948
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5882 - loss: 0.9956
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5877 - loss: 0.9963
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5872 - loss: 0.9969
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5869 - loss: 0.9974
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5865 - loss: 0.9980
119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5861 - loss: 0.9985
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5859 - loss: 0.9987
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5856 - loss: 0.9991
127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5853 - loss: 0.9995
130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5851 - loss: 0.9998
133/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5849 - loss: 1.0000
136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5848 - loss: 1.0001
138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5847 - loss: 1.0003
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5845 - loss: 1.0004
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5844 - loss: 1.0005
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5842 - loss: 1.0006
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5841 - loss: 1.0007
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5840 - loss: 1.0008
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5840 - loss: 1.0009
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5839 - loss: 1.0010
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5838 - loss: 1.0011
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5837 - loss: 1.0012
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5835 - loss: 1.0013
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5834 - loss: 1.0014
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5833 - loss: 1.0016
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5831 - loss: 1.0017
180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5830 - loss: 1.0018
183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5829 - loss: 1.0019
186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5827 - loss: 1.0021
189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5826 - loss: 1.0022
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5824 - loss: 1.0024
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5822 - loss: 1.0026
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5820 - loss: 1.0028
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5819 - loss: 1.0029
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5817 - loss: 1.0031
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5815 - loss: 1.0033
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5813 - loss: 1.0034
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5811 - loss: 1.0036
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5809 - loss: 1.0038
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5807 - loss: 1.0040
221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5806 - loss: 1.0042
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5804 - loss: 1.0043
226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5802 - loss: 1.0045
229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5800 - loss: 1.0047
232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5798 - loss: 1.0049
235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5797 - loss: 1.0051
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5795 - loss: 1.0052
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5794 - loss: 1.0053
242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5792 - loss: 1.0054
245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5791 - loss: 1.0056
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5788 - loss: 1.0058
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5787 - loss: 1.0060
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5785 - loss: 1.0061
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Epoch 27: val_accuracy improved from 0.59072 to 0.59092, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5723 - loss: 1.0111 - val_accuracy: 0.5909 - val_loss: 0.9703 - learning_rate: 4.0000e-04
Epoch 28/40
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4/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.7031 - loss: 0.9197
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Epoch 28: val_accuracy improved from 0.59092 to 0.59273, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5706 - loss: 1.0054 - val_accuracy: 0.5927 - val_loss: 0.9721 - learning_rate: 4.0000e-04
Epoch 29/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 164ms/step - accuracy: 0.7188 - loss: 0.8199
4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6348 - loss: 0.8995
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386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5770 - loss: 1.0077
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5770 - loss: 1.0077
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5770 - loss: 1.0078
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5769 - loss: 1.0078
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5768 - loss: 1.0079
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5768 - loss: 1.0079
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5767 - loss: 1.0080
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5767 - loss: 1.0080
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5766 - loss: 1.0081
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5766 - loss: 1.0081
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5765 - loss: 1.0082
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5765 - loss: 1.0082
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5764 - loss: 1.0082
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5763 - loss: 1.0083
426/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5763 - loss: 1.0083
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5762 - loss: 1.0083
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5762 - loss: 1.0084
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5762 - loss: 1.0084
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5761 - loss: 1.0084
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5761 - loss: 1.0085
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5760 - loss: 1.0085
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5760 - loss: 1.0085
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 1.0085
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 1.0085
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 1.0085
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 1.0085
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 1.0086
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 1.0086
Epoch 29: val_accuracy did not improve from 0.59273
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5757 - loss: 1.0086 - val_accuracy: 0.5913 - val_loss: 0.9718 - learning_rate: 4.0000e-04
Epoch 30/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.5312 - loss: 1.2266
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5156 - loss: 1.1374
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5179 - loss: 1.1166
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5214 - loss: 1.0958
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5278 - loss: 1.0755
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5332 - loss: 1.0597
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5365 - loss: 1.0495
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5388 - loss: 1.0434
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5400 - loss: 1.0397
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5415 - loss: 1.0358
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5429 - loss: 1.0325
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5440 - loss: 1.0296
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5447 - loss: 1.0276
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5453 - loss: 1.0260
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5457 - loss: 1.0249
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5458 - loss: 1.0242
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5460 - loss: 1.0239
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5461 - loss: 1.0237
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5459 - loss: 1.0239
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5458 - loss: 1.0241
61/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5458 - loss: 1.0241
64/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5460 - loss: 1.0240
67/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5460 - loss: 1.0242
70/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5462 - loss: 1.0241
73/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5466 - loss: 1.0240
76/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5470 - loss: 1.0238
79/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5475 - loss: 1.0236
82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5480 - loss: 1.0233
85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5484 - loss: 1.0232
88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5489 - loss: 1.0229
91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5493 - loss: 1.0227
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5496 - loss: 1.0225
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5499 - loss: 1.0224
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5501 - loss: 1.0223
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5503 - loss: 1.0221
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5505 - loss: 1.0220
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5507 - loss: 1.0219
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5510 - loss: 1.0218
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5512 - loss: 1.0218
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5515 - loss: 1.0217
119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5516 - loss: 1.0218
122/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5517 - loss: 1.0219
125/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5518 - loss: 1.0219
128/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5520 - loss: 1.0219
131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5522 - loss: 1.0219
134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5524 - loss: 1.0218
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5526 - loss: 1.0218
140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5527 - loss: 1.0218
143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5528 - loss: 1.0218
146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5529 - loss: 1.0218
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5530 - loss: 1.0218
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5532 - loss: 1.0219
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5533 - loss: 1.0219
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5534 - loss: 1.0219
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5535 - loss: 1.0219
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5536 - loss: 1.0219
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5536 - loss: 1.0219
170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5537 - loss: 1.0219
173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5538 - loss: 1.0219
176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5538 - loss: 1.0218
179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5539 - loss: 1.0218
182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5540 - loss: 1.0218
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5541 - loss: 1.0217
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5542 - loss: 1.0217
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5544 - loss: 1.0216
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5545 - loss: 1.0215
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5547 - loss: 1.0214
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5548 - loss: 1.0213
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5549 - loss: 1.0212
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5551 - loss: 1.0211
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5552 - loss: 1.0210
212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5553 - loss: 1.0210
215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5554 - loss: 1.0209
218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5554 - loss: 1.0208
221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5555 - loss: 1.0208
224/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5556 - loss: 1.0207
227/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5557 - loss: 1.0207
230/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5558 - loss: 1.0206
233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5559 - loss: 1.0205
235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5560 - loss: 1.0204
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5561 - loss: 1.0203
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5562 - loss: 1.0201
244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5563 - loss: 1.0200
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5564 - loss: 1.0199
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5566 - loss: 1.0197
253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5567 - loss: 1.0196
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5568 - loss: 1.0195
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5569 - loss: 1.0193
262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5570 - loss: 1.0192
265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5571 - loss: 1.0191
268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5573 - loss: 1.0190
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5574 - loss: 1.0188
274/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5575 - loss: 1.0187
277/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5576 - loss: 1.0186
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5577 - loss: 1.0185
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5578 - loss: 1.0184
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5579 - loss: 1.0183
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5581 - loss: 1.0182
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5582 - loss: 1.0181
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5583 - loss: 1.0180
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5584 - loss: 1.0179
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5585 - loss: 1.0178
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5586 - loss: 1.0177
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5587 - loss: 1.0176
308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5588 - loss: 1.0176
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5589 - loss: 1.0175
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5590 - loss: 1.0174
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5591 - loss: 1.0174
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5592 - loss: 1.0173
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5593 - loss: 1.0172
325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5594 - loss: 1.0171
328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5595 - loss: 1.0171
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5596 - loss: 1.0170
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5596 - loss: 1.0170
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5597 - loss: 1.0169
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5598 - loss: 1.0169
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0168
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0168
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5600 - loss: 1.0167
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5601 - loss: 1.0167
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5602 - loss: 1.0166
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5602 - loss: 1.0166
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5603 - loss: 1.0166
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5604 - loss: 1.0165
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5604 - loss: 1.0165
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5605 - loss: 1.0164
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5605 - loss: 1.0164
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5606 - loss: 1.0164
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5606 - loss: 1.0163
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5607 - loss: 1.0163
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5608 - loss: 1.0163
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5608 - loss: 1.0162
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5609 - loss: 1.0162
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5609 - loss: 1.0162
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5610 - loss: 1.0161
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5610 - loss: 1.0161
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5611 - loss: 1.0161
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5611 - loss: 1.0160
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5612 - loss: 1.0160
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5613 - loss: 1.0160
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5613 - loss: 1.0159
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5614 - loss: 1.0159
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5614 - loss: 1.0159
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5615 - loss: 1.0158
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5616 - loss: 1.0158
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5616 - loss: 1.0158
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5616 - loss: 1.0158
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5617 - loss: 1.0157
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5617 - loss: 1.0157
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5618 - loss: 1.0157
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5618 - loss: 1.0156
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5619 - loss: 1.0156
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5619 - loss: 1.0156
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5619 - loss: 1.0156
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5620 - loss: 1.0155
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5620 - loss: 1.0155
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5621 - loss: 1.0154
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5621 - loss: 1.0154
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5622 - loss: 1.0154
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5622 - loss: 1.0153
Epoch 30: val_accuracy did not improve from 0.59273
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5623 - loss: 1.0153 - val_accuracy: 0.5927 - val_loss: 0.9712 - learning_rate: 4.0000e-04
Epoch 31/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 159ms/step - accuracy: 0.5312 - loss: 0.9312
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5853 - loss: 0.9403
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5871 - loss: 0.9412
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5914 - loss: 0.9322
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5934 - loss: 0.9309
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5925 - loss: 0.9343
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5926 - loss: 0.9369
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5918 - loss: 0.9392
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5894 - loss: 0.9427
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5877 - loss: 0.9461
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5855 - loss: 0.9502
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5843 - loss: 0.9533
36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5834 - loss: 0.9558
39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5828 - loss: 0.9582
42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5822 - loss: 0.9600
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5817 - loss: 0.9619
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5812 - loss: 0.9637
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5810 - loss: 0.9650
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5808 - loss: 0.9662
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5806 - loss: 0.9675
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5801 - loss: 0.9690
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5798 - loss: 0.9699
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5795 - loss: 0.9707
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5792 - loss: 0.9717
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5789 - loss: 0.9728
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5785 - loss: 0.9737
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5783 - loss: 0.9742
77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5781 - loss: 0.9747
80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5779 - loss: 0.9753
83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5777 - loss: 0.9760
86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5776 - loss: 0.9766
89/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5774 - loss: 0.9771
92/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5772 - loss: 0.9777
95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5771 - loss: 0.9781
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5770 - loss: 0.9785
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5769 - loss: 0.9789
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5767 - loss: 0.9793
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5766 - loss: 0.9797
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5765 - loss: 0.9802
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5763 - loss: 0.9805
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5763 - loss: 0.9809
119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5762 - loss: 0.9812
122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5761 - loss: 0.9815
125/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5761 - loss: 0.9818
128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5761 - loss: 0.9820
131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5761 - loss: 0.9822
134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5762 - loss: 0.9823
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5761 - loss: 0.9825
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5761 - loss: 0.9828
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5760 - loss: 0.9830
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5759 - loss: 0.9833
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5758 - loss: 0.9835
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5758 - loss: 0.9836
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5757 - loss: 0.9838
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5756 - loss: 0.9840
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5755 - loss: 0.9842
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5754 - loss: 0.9843
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5753 - loss: 0.9844
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5753 - loss: 0.9845
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5752 - loss: 0.9846
177/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5751 - loss: 0.9847
180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5751 - loss: 0.9848
183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9849
186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9850
189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9850
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9851
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9852
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9853
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9854
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9854
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9855
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9856
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9857
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9857
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9858
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9859
225/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5750 - loss: 0.9860
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5750 - loss: 0.9861
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5750 - loss: 0.9863
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5750 - loss: 0.9864
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5750 - loss: 0.9865
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9866
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9867
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9868
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9869
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9870
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9871
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9872
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9873
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9874
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9875
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9875
273/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9876
276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9877
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9878
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9879
285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9880
288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9880
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9881
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9882
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9882
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9883
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9884
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9885
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9886
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9886
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9887
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9888
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9889
324/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5750 - loss: 0.9889
327/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5750 - loss: 0.9890
330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9890
333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9891
336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9891
339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9892
342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9893
345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9893
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9894
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9894
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9895
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9896
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9896
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9896
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5748 - loss: 0.9897
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5748 - loss: 0.9898
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5748 - loss: 0.9898
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9899
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9899
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9900
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9900
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9900
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9901
390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9901
393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9902
396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9902
399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9903
402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9903
405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9904
408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9904
411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9905
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9905
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5747 - loss: 0.9905
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5747 - loss: 0.9906
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9906
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9907
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9907
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9908
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9909
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9909
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9909
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9910
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9911
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9911
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9912
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9912
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9913
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9913
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9914
Epoch 31: val_accuracy did not improve from 0.59273
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5746 - loss: 0.9916 - val_accuracy: 0.5885 - val_loss: 0.9700 - learning_rate: 4.0000e-04
Epoch 32/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 164ms/step - accuracy: 0.5625 - loss: 0.9749
4/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5833 - loss: 0.9748
7/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5794 - loss: 0.9779
10/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5792 - loss: 0.9819
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5802 - loss: 0.9873
16/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5785 - loss: 0.9920
18/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5768 - loss: 0.9949
21/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5750 - loss: 0.9973
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5734 - loss: 0.9995
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5725 - loss: 1.0002
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5714 - loss: 1.0014
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5704 - loss: 1.0030
36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5696 - loss: 1.0043
39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5694 - loss: 1.0053
41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5693 - loss: 1.0056
44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5691 - loss: 1.0058
47/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5690 - loss: 1.0061
50/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5688 - loss: 1.0064
53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5686 - loss: 1.0069
56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5685 - loss: 1.0071
59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5684 - loss: 1.0075
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5683 - loss: 1.0080
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418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5698 - loss: 1.0053
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424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5698 - loss: 1.0053
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5698 - loss: 1.0054
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5698 - loss: 1.0054
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5698 - loss: 1.0054
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5697 - loss: 1.0054
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5697 - loss: 1.0054
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5697 - loss: 1.0054
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5697 - loss: 1.0054
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5697 - loss: 1.0054
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5697 - loss: 1.0054
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5697 - loss: 1.0054
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5697 - loss: 1.0054
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5697 - loss: 1.0054
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5697 - loss: 1.0054
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5697 - loss: 1.0054
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5697 - loss: 1.0054
Epoch 32: val_accuracy did not improve from 0.59273
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5697 - loss: 1.0054 - val_accuracy: 0.5861 - val_loss: 0.9732 - learning_rate: 4.0000e-04
Epoch 33/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:22 175ms/step - accuracy: 0.5625 - loss: 0.9450
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5586 - loss: 0.9447
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5623 - loss: 0.9680
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5609 - loss: 0.9812
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5593 - loss: 0.9928
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5573 - loss: 1.0004
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5554 - loss: 1.0053
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5547 - loss: 1.0067
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5556 - loss: 1.0054
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5559 - loss: 1.0050
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5566 - loss: 1.0042
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5574 - loss: 1.0035
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5582 - loss: 1.0030
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5590 - loss: 1.0023
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5596 - loss: 1.0016
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5602 - loss: 1.0007
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5605 - loss: 1.0005
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5608 - loss: 1.0002
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5610 - loss: 1.0001
59/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5612 - loss: 1.0002
62/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5612 - loss: 1.0005
65/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5613 - loss: 1.0007
68/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5612 - loss: 1.0010
71/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5611 - loss: 1.0015
74/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5609 - loss: 1.0019
77/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5608 - loss: 1.0024
80/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5608 - loss: 1.0028
83/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0032
86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0033
89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0034
92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0035
95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0036
98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0037
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0039
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0042
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0044
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0047
113/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5608 - loss: 1.0049
116/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5608 - loss: 1.0051
119/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5609 - loss: 1.0053
122/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5609 - loss: 1.0055
125/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5610 - loss: 1.0057
128/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5610 - loss: 1.0058
131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5610 - loss: 1.0060
134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5610 - loss: 1.0061
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5611 - loss: 1.0062
140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5611 - loss: 1.0063
143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5612 - loss: 1.0064
146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5612 - loss: 1.0065
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5613 - loss: 1.0065
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5613 - loss: 1.0066
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5613 - loss: 1.0068
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5613 - loss: 1.0069
161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5614 - loss: 1.0071
164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5614 - loss: 1.0072
167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5614 - loss: 1.0073
170/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5615 - loss: 1.0075
173/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5616 - loss: 1.0076
176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5616 - loss: 1.0077
179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5617 - loss: 1.0078
182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0079
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0080
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0081
190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5620 - loss: 1.0082
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5620 - loss: 1.0083
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0084
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0085
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0086
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0087
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5623 - loss: 1.0088
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5624 - loss: 1.0089
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5625 - loss: 1.0090
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5625 - loss: 1.0090
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5626 - loss: 1.0091
221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5626 - loss: 1.0092
223/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5627 - loss: 1.0092
226/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5627 - loss: 1.0093
229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5627 - loss: 1.0094
232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5628 - loss: 1.0095
235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5628 - loss: 1.0096
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5629 - loss: 1.0097
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5629 - loss: 1.0098
244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5629 - loss: 1.0099
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5630 - loss: 1.0100
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5630 - loss: 1.0101
253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5630 - loss: 1.0102
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5631 - loss: 1.0103
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5631 - loss: 1.0103
262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5631 - loss: 1.0104
265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5631 - loss: 1.0105
268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5632 - loss: 1.0105
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5632 - loss: 1.0106
274/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5633 - loss: 1.0106
276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5633 - loss: 1.0106
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5633 - loss: 1.0107
281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5633 - loss: 1.0107
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5633 - loss: 1.0108
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5633 - loss: 1.0109
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5634 - loss: 1.0109
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5634 - loss: 1.0109
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5634 - loss: 1.0110
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5634 - loss: 1.0110
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5634 - loss: 1.0110
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5634 - loss: 1.0110
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5634 - loss: 1.0110
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5634 - loss: 1.0110
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5635 - loss: 1.0110
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5635 - loss: 1.0110
317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5635 - loss: 1.0110
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5635 - loss: 1.0110
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5635 - loss: 1.0110
326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5636 - loss: 1.0110
329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5636 - loss: 1.0110
332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5636 - loss: 1.0110
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5636 - loss: 1.0110
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5636 - loss: 1.0110
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5637 - loss: 1.0110
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5637 - loss: 1.0110
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5637 - loss: 1.0110
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5637 - loss: 1.0110
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5638 - loss: 1.0110
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5638 - loss: 1.0110
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5638 - loss: 1.0110
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5638 - loss: 1.0110
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5638 - loss: 1.0110
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5639 - loss: 1.0110
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5639 - loss: 1.0110
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5639 - loss: 1.0110
374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5639 - loss: 1.0110
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5639 - loss: 1.0110
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5639 - loss: 1.0110
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5639 - loss: 1.0110
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5639 - loss: 1.0110
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5640 - loss: 1.0110
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5640 - loss: 1.0110
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5640 - loss: 1.0110
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5640 - loss: 1.0110
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5640 - loss: 1.0111
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5640 - loss: 1.0111
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5641 - loss: 1.0111
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5641 - loss: 1.0111
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5641 - loss: 1.0111
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5641 - loss: 1.0110
Epoch 33: val_accuracy did not improve from 0.59273
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5641 - loss: 1.0110 - val_accuracy: 0.5901 - val_loss: 0.9703 - learning_rate: 4.0000e-04
Epoch 34/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:30 191ms/step - accuracy: 0.6875 - loss: 0.9269
4/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6895 - loss: 0.8722
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6525 - loss: 0.9065
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6380 - loss: 0.9201
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6330 - loss: 0.9261
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6271 - loss: 0.9353
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6226 - loss: 0.9407
22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6184 - loss: 0.9448
25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6137 - loss: 0.9498
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6098 - loss: 0.9539
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6063 - loss: 0.9574
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6030 - loss: 0.9604
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6002 - loss: 0.9629
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5981 - loss: 0.9647
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5968 - loss: 0.9660
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5948 - loss: 0.9680
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5931 - loss: 0.9700
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5917 - loss: 0.9716
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5903 - loss: 0.9731
56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5896 - loss: 0.9740
59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5886 - loss: 0.9750
62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5877 - loss: 0.9759
65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5870 - loss: 0.9764
68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5863 - loss: 0.9770
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5858 - loss: 0.9774
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5851 - loss: 0.9780
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5846 - loss: 0.9785
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5840 - loss: 0.9792
82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5835 - loss: 0.9798
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5830 - loss: 0.9803
88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5825 - loss: 0.9809
91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5821 - loss: 0.9814
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5816 - loss: 0.9819
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5812 - loss: 0.9823
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5808 - loss: 0.9828
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5803 - loss: 0.9833
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5799 - loss: 0.9837
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5795 - loss: 0.9841
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5792 - loss: 0.9844
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5787 - loss: 0.9849
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5783 - loss: 0.9853
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5779 - loss: 0.9857
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5776 - loss: 0.9860
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5772 - loss: 0.9865
129/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5768 - loss: 0.9871
132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5764 - loss: 0.9877
135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5760 - loss: 0.9882
138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5757 - loss: 0.9888
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5753 - loss: 0.9894
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5749 - loss: 0.9899
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5746 - loss: 0.9905
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5743 - loss: 0.9910
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5740 - loss: 0.9915
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5737 - loss: 0.9920
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5735 - loss: 0.9924
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5733 - loss: 0.9927
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5731 - loss: 0.9931
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5730 - loss: 0.9934
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5728 - loss: 0.9936
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5727 - loss: 0.9939
176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5726 - loss: 0.9941
179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5725 - loss: 0.9944
182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5724 - loss: 0.9946
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5723 - loss: 0.9949
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5722 - loss: 0.9951
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5721 - loss: 0.9954
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5720 - loss: 0.9956
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5719 - loss: 0.9959
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5718 - loss: 0.9961
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5718 - loss: 0.9963
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5717 - loss: 0.9964
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5717 - loss: 0.9966
212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5717 - loss: 0.9967
215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5716 - loss: 0.9969
218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5716 - loss: 0.9970
221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5715 - loss: 0.9972
224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5715 - loss: 0.9974
227/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5714 - loss: 0.9977
229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5713 - loss: 0.9978
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5713 - loss: 0.9980
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5712 - loss: 0.9982
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5711 - loss: 0.9985
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5710 - loss: 0.9987
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5709 - loss: 0.9989
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5708 - loss: 0.9991
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5707 - loss: 0.9993
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5706 - loss: 0.9995
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5706 - loss: 0.9997
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5705 - loss: 0.9999
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5704 - loss: 1.0001
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5703 - loss: 1.0003
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5702 - loss: 1.0006
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5702 - loss: 1.0007
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5701 - loss: 1.0009
276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5700 - loss: 1.0011
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5699 - loss: 1.0013
281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5699 - loss: 1.0014
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5698 - loss: 1.0016
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5697 - loss: 1.0017
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5696 - loss: 1.0019
294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5695 - loss: 1.0021
297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5694 - loss: 1.0023
300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5694 - loss: 1.0025
303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5693 - loss: 1.0026
306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5692 - loss: 1.0028
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5692 - loss: 1.0029
312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5691 - loss: 1.0031
315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5690 - loss: 1.0032
318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5689 - loss: 1.0033
321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5689 - loss: 1.0035
324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5688 - loss: 1.0036
327/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5687 - loss: 1.0038
330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5686 - loss: 1.0039
333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5686 - loss: 1.0040
336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5685 - loss: 1.0042
339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5684 - loss: 1.0043
342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5684 - loss: 1.0044
345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5683 - loss: 1.0046
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5682 - loss: 1.0047
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5682 - loss: 1.0048
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5681 - loss: 1.0049
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5680 - loss: 1.0051
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5680 - loss: 1.0052
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5679 - loss: 1.0053
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5679 - loss: 1.0054
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5679 - loss: 1.0055
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5678 - loss: 1.0056
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5678 - loss: 1.0057
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5677 - loss: 1.0058
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5677 - loss: 1.0059
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5677 - loss: 1.0060
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5676 - loss: 1.0060
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5676 - loss: 1.0061
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5676 - loss: 1.0062
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5675 - loss: 1.0063
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5675 - loss: 1.0064
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5675 - loss: 1.0065
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5674 - loss: 1.0066
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5674 - loss: 1.0066
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5674 - loss: 1.0067
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5673 - loss: 1.0068
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5673 - loss: 1.0069
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5673 - loss: 1.0070
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5672 - loss: 1.0070
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5672 - loss: 1.0071
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5671 - loss: 1.0072
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5671 - loss: 1.0073
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5671 - loss: 1.0073
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5671 - loss: 1.0074
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5670 - loss: 1.0075
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5670 - loss: 1.0075
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5670 - loss: 1.0076
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5669 - loss: 1.0077
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5669 - loss: 1.0077
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5669 - loss: 1.0078
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5668 - loss: 1.0079
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5668 - loss: 1.0079
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5668 - loss: 1.0080
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5667 - loss: 1.0081
Epoch 34: val_accuracy did not improve from 0.59273
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5667 - loss: 1.0082 - val_accuracy: 0.5897 - val_loss: 0.9754 - learning_rate: 4.0000e-04
Epoch 35/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 162ms/step - accuracy: 0.6875 - loss: 0.8332
4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6133 - loss: 0.9275
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6044 - loss: 0.9532
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6025 - loss: 0.9573
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6018 - loss: 0.9592
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6010 - loss: 0.9611
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6003 - loss: 0.9630
22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6001 - loss: 0.9647
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6000 - loss: 0.9656
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5983 - loss: 0.9682
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5969 - loss: 0.9704
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5960 - loss: 0.9723
36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5953 - loss: 0.9739
39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5950 - loss: 0.9751
42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5949 - loss: 0.9762
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5946 - loss: 0.9777
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5940 - loss: 0.9793
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5932 - loss: 0.9808
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5924 - loss: 0.9819
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5918 - loss: 0.9828
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5912 - loss: 0.9836
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5905 - loss: 0.9845
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5898 - loss: 0.9855
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5892 - loss: 0.9863
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5888 - loss: 0.9869
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5884 - loss: 0.9874
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5881 - loss: 0.9878
81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5879 - loss: 0.9882
84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5877 - loss: 0.9885
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444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 1.0035
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 1.0036
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 1.0036
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 1.0036
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5782 - loss: 1.0037
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5782 - loss: 1.0037
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5782 - loss: 1.0038
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5782 - loss: 1.0038
Epoch 35: val_accuracy did not improve from 0.59273
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5781 - loss: 1.0039 - val_accuracy: 0.5885 - val_loss: 0.9745 - learning_rate: 4.0000e-04
Epoch 36/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:20 171ms/step - accuracy: 0.7500 - loss: 0.8510
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.6536 - loss: 0.9619
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6405 - loss: 0.9682
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6257 - loss: 0.9732
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6197 - loss: 0.9722
15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6168 - loss: 0.9717
18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6136 - loss: 0.9713
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6108 - loss: 0.9710
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6090 - loss: 0.9707
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6079 - loss: 0.9712
30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6062 - loss: 0.9724
33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6047 - loss: 0.9738
36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6035 - loss: 0.9745
39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6023 - loss: 0.9754
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6013 - loss: 0.9759
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6000 - loss: 0.9766
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5988 - loss: 0.9771
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5980 - loss: 0.9773
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5971 - loss: 0.9775
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5963 - loss: 0.9776
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5955 - loss: 0.9780
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5947 - loss: 0.9783
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5940 - loss: 0.9785
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5934 - loss: 0.9788
72/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5926 - loss: 0.9791
75/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5919 - loss: 0.9794
78/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5911 - loss: 0.9797
81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5904 - loss: 0.9801
84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5897 - loss: 0.9807
87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5890 - loss: 0.9813
90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5884 - loss: 0.9819
93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5878 - loss: 0.9824
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5873 - loss: 0.9830
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5868 - loss: 0.9835
101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5864 - loss: 0.9839
104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5860 - loss: 0.9844
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5855 - loss: 0.9850
110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5850 - loss: 0.9855
113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5846 - loss: 0.9860
116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5841 - loss: 0.9865
119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5837 - loss: 0.9870
122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5833 - loss: 0.9875
125/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5829 - loss: 0.9880
128/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5825 - loss: 0.9885
131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5821 - loss: 0.9890
134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5817 - loss: 0.9896
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5814 - loss: 0.9900
140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5811 - loss: 0.9904
143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5808 - loss: 0.9909
145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5806 - loss: 0.9911
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5804 - loss: 0.9914
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5800 - loss: 0.9918
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5797 - loss: 0.9922
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5795 - loss: 0.9925
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5793 - loss: 0.9927
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5791 - loss: 0.9931
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5788 - loss: 0.9934
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5786 - loss: 0.9937
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5784 - loss: 0.9940
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5783 - loss: 0.9942
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5781 - loss: 0.9944
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5780 - loss: 0.9946
180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5778 - loss: 0.9949
182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5777 - loss: 0.9950
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5775 - loss: 0.9952
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5774 - loss: 0.9953
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5773 - loss: 0.9955
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5772 - loss: 0.9957
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5771 - loss: 0.9958
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5770 - loss: 0.9960
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5770 - loss: 0.9960
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5769 - loss: 0.9961
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5768 - loss: 0.9962
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5767 - loss: 0.9963
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5767 - loss: 0.9964
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5766 - loss: 0.9964
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5766 - loss: 0.9965
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5766 - loss: 0.9966
226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5765 - loss: 0.9966
229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5765 - loss: 0.9967
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5764 - loss: 0.9968
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5764 - loss: 0.9968
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5763 - loss: 0.9969
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5763 - loss: 0.9970
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5762 - loss: 0.9971
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5762 - loss: 0.9972
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5761 - loss: 0.9972
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5761 - loss: 0.9973
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5760 - loss: 0.9973
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5760 - loss: 0.9974
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5759 - loss: 0.9974
264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5759 - loss: 0.9975
267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5759 - loss: 0.9975
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5758 - loss: 0.9976
273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5758 - loss: 0.9977
276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5757 - loss: 0.9977
279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5757 - loss: 0.9978
282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5756 - loss: 0.9979
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5756 - loss: 0.9979
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5756 - loss: 0.9979
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5755 - loss: 0.9980
292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5754 - loss: 0.9981
295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5754 - loss: 0.9982
298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5753 - loss: 0.9982
301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5753 - loss: 0.9983
304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5752 - loss: 0.9984
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5751 - loss: 0.9985
310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9986
313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9986
316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5749 - loss: 0.9987
319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5748 - loss: 0.9988
322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5748 - loss: 0.9989
325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5747 - loss: 0.9990
328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5746 - loss: 0.9990
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5745 - loss: 0.9991
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5745 - loss: 0.9992
336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5744 - loss: 0.9993
339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5744 - loss: 0.9993
342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5743 - loss: 0.9994
345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5742 - loss: 0.9995
348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5741 - loss: 0.9996
351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5741 - loss: 0.9997
354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5740 - loss: 0.9998
357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5739 - loss: 0.9999
360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5739 - loss: 1.0000
363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5738 - loss: 1.0001
366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5738 - loss: 1.0001
369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5737 - loss: 1.0002
372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5736 - loss: 1.0003
375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5736 - loss: 1.0004
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5736 - loss: 1.0004
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5735 - loss: 1.0005
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5735 - loss: 1.0005
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5734 - loss: 1.0006
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5734 - loss: 1.0007
392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5733 - loss: 1.0007
395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5733 - loss: 1.0008
398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5732 - loss: 1.0009
401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5732 - loss: 1.0009
404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5731 - loss: 1.0010
407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5731 - loss: 1.0010
410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5730 - loss: 1.0011
413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5730 - loss: 1.0011
416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5729 - loss: 1.0012
419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5729 - loss: 1.0012
422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5728 - loss: 1.0012
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5728 - loss: 1.0013
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5727 - loss: 1.0013
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5727 - loss: 1.0014
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5727 - loss: 1.0014
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0015
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0015
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0015
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0016
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5724 - loss: 1.0016
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5724 - loss: 1.0017
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5724 - loss: 1.0017
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5723 - loss: 1.0017
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5723 - loss: 1.0018
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5722 - loss: 1.0018
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5722 - loss: 1.0018
Epoch 36: ReduceLROnPlateau reducing learning rate to 7.999999215826393e-05.
Epoch 36: val_accuracy did not improve from 0.59273
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5721 - loss: 1.0019 - val_accuracy: 0.5913 - val_loss: 0.9738 - learning_rate: 4.0000e-04
Epoch 37/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 162ms/step - accuracy: 0.5938 - loss: 0.9484
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5540 - loss: 1.0645
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5577 - loss: 1.0545
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5652 - loss: 1.0372
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5677 - loss: 1.0355
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5685 - loss: 1.0344
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5674 - loss: 1.0341
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5667 - loss: 1.0327
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5666 - loss: 1.0318
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5666 - loss: 1.0309
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5670 - loss: 1.0294
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5675 - loss: 1.0280
37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5677 - loss: 1.0270
40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0263
43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0259
46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5678 - loss: 1.0257
49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5679 - loss: 1.0251
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0244
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0242
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0241
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5678 - loss: 1.0240
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5678 - loss: 1.0238
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0236
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0234
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5680 - loss: 1.0230
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5681 - loss: 1.0227
78/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5682 - loss: 1.0223
81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5684 - loss: 1.0218
84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5686 - loss: 1.0212
87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5689 - loss: 1.0207
90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5692 - loss: 1.0201
93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5695 - loss: 1.0195
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5697 - loss: 1.0189
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5700 - loss: 1.0184
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5702 - loss: 1.0178
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5704 - loss: 1.0173
107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5705 - loss: 1.0169
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5706 - loss: 1.0166
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5707 - loss: 1.0161
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5709 - loss: 1.0156
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5711 - loss: 1.0152
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5712 - loss: 1.0148
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5713 - loss: 1.0145
127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5714 - loss: 1.0141
130/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5715 - loss: 1.0139
133/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5715 - loss: 1.0136
136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5716 - loss: 1.0133
139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5717 - loss: 1.0131
142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5717 - loss: 1.0128
145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5718 - loss: 1.0125
148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5718 - loss: 1.0123
151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5719 - loss: 1.0120
154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5719 - loss: 1.0117
157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5720 - loss: 1.0115
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5721 - loss: 1.0112
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5721 - loss: 1.0110
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5722 - loss: 1.0108
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5722 - loss: 1.0107
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5722 - loss: 1.0105
175/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5722 - loss: 1.0103
178/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5722 - loss: 1.0101
181/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5723 - loss: 1.0099
184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5723 - loss: 1.0097
187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5724 - loss: 1.0095
190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5724 - loss: 1.0093
193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5725 - loss: 1.0091
196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5725 - loss: 1.0090
199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5725 - loss: 1.0088
202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5726 - loss: 1.0086
205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5726 - loss: 1.0085
208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5727 - loss: 1.0083
211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5727 - loss: 1.0081
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5728 - loss: 1.0080
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5728 - loss: 1.0078
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5729 - loss: 1.0076
223/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5729 - loss: 1.0075
226/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5730 - loss: 1.0074
229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5730 - loss: 1.0072
232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5730 - loss: 1.0071
235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5731 - loss: 1.0070
238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5731 - loss: 1.0069
241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5731 - loss: 1.0069
244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5731 - loss: 1.0068
247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0067
250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0066
253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0065
256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0064
259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0064
262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0064
265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0063
268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0063
270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0063
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0063
274/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063
277/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0062
280/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0062
283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063
302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063
305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063
307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063
309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063
311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063
317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063
325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0063
328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0063
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0063
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0063
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0062
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0062
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0062
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0062
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0062
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0061
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0061
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0061
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0061
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0060
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0060
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0060
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0059
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059
397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0058
400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0058
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5729 - loss: 1.0058
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5729 - loss: 1.0058
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5729 - loss: 1.0058
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5729 - loss: 1.0058
414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5729 - loss: 1.0058
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5729 - loss: 1.0059
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5728 - loss: 1.0059
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5728 - loss: 1.0059
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5728 - loss: 1.0059
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5728 - loss: 1.0059
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5728 - loss: 1.0059
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5727 - loss: 1.0059
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5727 - loss: 1.0060
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5727 - loss: 1.0060
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0060
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0060
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0060
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0061
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0061
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0061
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0061
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0061
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5724 - loss: 1.0061
Epoch 37: val_accuracy did not improve from 0.59273
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5724 - loss: 1.0062 - val_accuracy: 0.5913 - val_loss: 0.9698 - learning_rate: 8.0000e-05
Epoch 38/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:13 157ms/step - accuracy: 0.5000 - loss: 1.0527
4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4915 - loss: 1.0061
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5249 - loss: 0.9704
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5351 - loss: 0.9633
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5427 - loss: 0.9561
16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5487 - loss: 0.9557
18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5527 - loss: 0.9548
21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5579 - loss: 0.9540
24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5614 - loss: 0.9543
27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5640 - loss: 0.9551
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5654 - loss: 0.9566
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5657 - loss: 0.9591
36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5662 - loss: 0.9617
39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5669 - loss: 0.9641
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5679 - loss: 0.9655
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5689 - loss: 0.9667
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5698 - loss: 0.9677
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5704 - loss: 0.9688
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5709 - loss: 0.9697
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5714 - loss: 0.9705
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5719 - loss: 0.9713
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5725 - loss: 0.9717
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5731 - loss: 0.9723
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5737 - loss: 0.9729
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5741 - loss: 0.9737
75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5743 - loss: 0.9745
78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5746 - loss: 0.9751
80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5748 - loss: 0.9755
82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5750 - loss: 0.9759
85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5753 - loss: 0.9764
88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5756 - loss: 0.9769
91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5758 - loss: 0.9772
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5760 - loss: 0.9776
96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5762 - loss: 0.9778
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5764 - loss: 0.9782
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5765 - loss: 0.9785
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5767 - loss: 0.9788
108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5769 - loss: 0.9790
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5771 - loss: 0.9792
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5773 - loss: 0.9795
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5774 - loss: 0.9798
120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5775 - loss: 0.9800
123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5777 - loss: 0.9803
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5778 - loss: 0.9805
128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5779 - loss: 0.9806
131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5780 - loss: 0.9808
134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5781 - loss: 0.9810
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5782 - loss: 0.9812
140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5783 - loss: 0.9814
143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5783 - loss: 0.9817
146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5784 - loss: 0.9819
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5784 - loss: 0.9821
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5785 - loss: 0.9823
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5785 - loss: 0.9825
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5786 - loss: 0.9827
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5786 - loss: 0.9828
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5786 - loss: 0.9829
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5786 - loss: 0.9830
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5786 - loss: 0.9832
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5787 - loss: 0.9833
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5787 - loss: 0.9835
177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5787 - loss: 0.9836
180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5787 - loss: 0.9838
183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5788 - loss: 0.9839
186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9840
188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9841
191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9843
194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9844
197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9846
200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9848
203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9850
206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9852
209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9854
212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9856
214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9857
217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9859
220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5787 - loss: 0.9861
223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5787 - loss: 0.9862
225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5787 - loss: 0.9863
227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5787 - loss: 0.9865
230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5787 - loss: 0.9866
233/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5787 - loss: 0.9868
236/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5786 - loss: 0.9870
239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5786 - loss: 0.9872
242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5786 - loss: 0.9873
245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5785 - loss: 0.9875
248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5785 - loss: 0.9877
251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5785 - loss: 0.9878
254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5784 - loss: 0.9880
257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5784 - loss: 0.9881
260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5783 - loss: 0.9883
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5783 - loss: 0.9885
266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5782 - loss: 0.9886
269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5782 - loss: 0.9888
272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5781 - loss: 0.9890
275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5781 - loss: 0.9891
278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5780 - loss: 0.9893
281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5780 - loss: 0.9894
284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5779 - loss: 0.9896
287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5779 - loss: 0.9897
290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5778 - loss: 0.9898
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5778 - loss: 0.9900
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5777 - loss: 0.9901
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5777 - loss: 0.9902
302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5777 - loss: 0.9903
305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5776 - loss: 0.9905
308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5776 - loss: 0.9906
311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5775 - loss: 0.9907
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5775 - loss: 0.9908
317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5774 - loss: 0.9909
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5774 - loss: 0.9911
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5773 - loss: 0.9912
326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5773 - loss: 0.9913
329/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5772 - loss: 0.9914
332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5772 - loss: 0.9916
335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5771 - loss: 0.9917
338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5771 - loss: 0.9918
341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5771 - loss: 0.9919
344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5770 - loss: 0.9920
347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5770 - loss: 0.9922
350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5769 - loss: 0.9923
353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5769 - loss: 0.9924
356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5768 - loss: 0.9925
359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5768 - loss: 0.9926
362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5768 - loss: 0.9927
365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5767 - loss: 0.9929
368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5767 - loss: 0.9930
371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5767 - loss: 0.9931
374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5766 - loss: 0.9932
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5766 - loss: 0.9933
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5766 - loss: 0.9934
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5765 - loss: 0.9935
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5765 - loss: 0.9936
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5765 - loss: 0.9937
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5764 - loss: 0.9938
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5764 - loss: 0.9939
397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5764 - loss: 0.9940
400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5763 - loss: 0.9941
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5763 - loss: 0.9942
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5763 - loss: 0.9943
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9944
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9944
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9945
417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9946
420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5761 - loss: 0.9947
423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5761 - loss: 0.9948
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5761 - loss: 0.9948
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5761 - loss: 0.9949
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5760 - loss: 0.9950
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5760 - loss: 0.9951
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5760 - loss: 0.9951
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5760 - loss: 0.9952
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 0.9953
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 0.9954
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 0.9955
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9955
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9956
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9957
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9958
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9959
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5756 - loss: 0.9960
Epoch 38: val_accuracy did not improve from 0.59273
473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5756 - loss: 0.9961 - val_accuracy: 0.5913 - val_loss: 0.9712 - learning_rate: 8.0000e-05
Epoch 39/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.5938 - loss: 1.0910
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5605 - loss: 1.1036
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5415 - loss: 1.1154
10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5448 - loss: 1.1067
13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5479 - loss: 1.0973
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5493 - loss: 1.0903
19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5512 - loss: 1.0844
22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5514 - loss: 1.0789
25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5509 - loss: 1.0754
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5500 - loss: 1.0734
30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5495 - loss: 1.0723
33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5490 - loss: 1.0703
36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5492 - loss: 1.0681
39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5497 - loss: 1.0656
42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5502 - loss: 1.0632
45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5506 - loss: 1.0612
48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5509 - loss: 1.0593
51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5510 - loss: 1.0577
54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5513 - loss: 1.0561
57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5516 - loss: 1.0543
60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5519 - loss: 1.0527
63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5521 - loss: 1.0512
66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5524 - loss: 1.0497
69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5526 - loss: 1.0482
72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5528 - loss: 1.0468
75/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5531 - loss: 1.0453
78/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5535 - loss: 1.0437
81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5540 - loss: 1.0423
84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5543 - loss: 1.0410
87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5547 - loss: 1.0398
90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5551 - loss: 1.0385
93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5554 - loss: 1.0374
96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5557 - loss: 1.0364
99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5559 - loss: 1.0355
102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5562 - loss: 1.0346
105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5564 - loss: 1.0338
108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5567 - loss: 1.0331
111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5569 - loss: 1.0323
114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5571 - loss: 1.0317
117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5572 - loss: 1.0311
120/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5575 - loss: 1.0304
123/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5577 - loss: 1.0297
126/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5580 - loss: 1.0291
129/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5582 - loss: 1.0284
132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5584 - loss: 1.0278
135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5587 - loss: 1.0271
137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5589 - loss: 1.0267
140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5591 - loss: 1.0261
143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5593 - loss: 1.0256
146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5594 - loss: 1.0251
149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5596 - loss: 1.0246
152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5598 - loss: 1.0242
155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5599 - loss: 1.0238
158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5601 - loss: 1.0233
160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5602 - loss: 1.0230
163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5604 - loss: 1.0226
166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5605 - loss: 1.0222
169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5606 - loss: 1.0219
172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5608 - loss: 1.0216
176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5609 - loss: 1.0211
179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5611 - loss: 1.0208
182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5612 - loss: 1.0204
185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5614 - loss: 1.0201
187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5614 - loss: 1.0198
189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5615 - loss: 1.0196
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5617 - loss: 1.0193
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0190
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0188
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0185
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5620 - loss: 1.0183
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0180
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0178
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0175
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5623 - loss: 1.0173
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5624 - loss: 1.0171
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5624 - loss: 1.0169
225/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5625 - loss: 1.0167
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5626 - loss: 1.0165
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5626 - loss: 1.0163
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5627 - loss: 1.0162
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5627 - loss: 1.0160
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5627 - loss: 1.0158
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5628 - loss: 1.0157
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5628 - loss: 1.0155
249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5629 - loss: 1.0154
252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5629 - loss: 1.0153
255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5630 - loss: 1.0151
258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5630 - loss: 1.0150
261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5631 - loss: 1.0149
263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5631 - loss: 1.0148
265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5631 - loss: 1.0147
268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5632 - loss: 1.0146
271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5632 - loss: 1.0145
274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5633 - loss: 1.0143
277/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5633 - loss: 1.0142
280/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5634 - loss: 1.0141
283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5634 - loss: 1.0140
286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5635 - loss: 1.0138
289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5635 - loss: 1.0137
291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5635 - loss: 1.0136
293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5636 - loss: 1.0135
296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5636 - loss: 1.0134
299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5637 - loss: 1.0133
302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5637 - loss: 1.0132
305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5638 - loss: 1.0131
308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5638 - loss: 1.0130
311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5639 - loss: 1.0129
314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5639 - loss: 1.0128
317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5639 - loss: 1.0127
320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5640 - loss: 1.0126
323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5640 - loss: 1.0125
325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5640 - loss: 1.0124
328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0123
331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0122
334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5642 - loss: 1.0121
337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5642 - loss: 1.0121
340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5642 - loss: 1.0120
343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5643 - loss: 1.0119
346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5643 - loss: 1.0118
349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5643 - loss: 1.0118
352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5643 - loss: 1.0117
355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5644 - loss: 1.0116
358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5644 - loss: 1.0115
361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5644 - loss: 1.0115
364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5644 - loss: 1.0114
367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5645 - loss: 1.0113
370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5645 - loss: 1.0112
373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5645 - loss: 1.0112
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5645 - loss: 1.0111
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5645 - loss: 1.0110
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5646 - loss: 1.0109
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5646 - loss: 1.0109
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5646 - loss: 1.0108
391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5646 - loss: 1.0107
394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5647 - loss: 1.0107
397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5647 - loss: 1.0106
400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5647 - loss: 1.0106
403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5647 - loss: 1.0105
406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5647 - loss: 1.0104
409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5648 - loss: 1.0104
412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5648 - loss: 1.0103
415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5648 - loss: 1.0103
418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5648 - loss: 1.0102
421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5648 - loss: 1.0102
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0101
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0101
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0100
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0100
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0100
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0099
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0099
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0098
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0098
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0098
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0097
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0097
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5650 - loss: 1.0097
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5650 - loss: 1.0096
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5650 - loss: 1.0096
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5650 - loss: 1.0095
473/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5650 - loss: 1.0094
Epoch 39: val_accuracy did not improve from 0.59273
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5650 - loss: 1.0094 - val_accuracy: 0.5897 - val_loss: 0.9716 - learning_rate: 8.0000e-05
Epoch 40/40
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 159ms/step - accuracy: 0.5000 - loss: 1.1003
4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5365 - loss: 1.0657
7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5480 - loss: 1.0649
10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5537 - loss: 1.0540
13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5573 - loss: 1.0479
16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5622 - loss: 1.0424
19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5646 - loss: 1.0375
22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5655 - loss: 1.0338
25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5654 - loss: 1.0318
28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5645 - loss: 1.0315
31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5639 - loss: 1.0308
34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5627 - loss: 1.0306
36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5621 - loss: 1.0303
38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5616 - loss: 1.0300
40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5611 - loss: 1.0299
43/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5603 - loss: 1.0299
46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5594 - loss: 1.0302
49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5585 - loss: 1.0305
52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5576 - loss: 1.0310
55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5570 - loss: 1.0313
58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5567 - loss: 1.0313
61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5565 - loss: 1.0312
64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5564 - loss: 1.0313
67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5563 - loss: 1.0312
70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5563 - loss: 1.0310
73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5564 - loss: 1.0306
76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5568 - loss: 1.0299
79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5572 - loss: 1.0293
82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5576 - loss: 1.0287
85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5579 - loss: 1.0283
88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5583 - loss: 1.0278
91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5586 - loss: 1.0274
94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5589 - loss: 1.0270
97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5591 - loss: 1.0268
100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5593 - loss: 1.0265
103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5595 - loss: 1.0262
106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5597 - loss: 1.0260
109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5598 - loss: 1.0259
112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5600 - loss: 1.0257
115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5602 - loss: 1.0255
118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5603 - loss: 1.0252
121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5605 - loss: 1.0250
124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5607 - loss: 1.0247
126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5608 - loss: 1.0246
129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5609 - loss: 1.0244
132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5610 - loss: 1.0242
135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5611 - loss: 1.0240
138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5612 - loss: 1.0239
141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5613 - loss: 1.0237
144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5614 - loss: 1.0234
147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5615 - loss: 1.0232
150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5616 - loss: 1.0231
153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5617 - loss: 1.0229
156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5617 - loss: 1.0228
159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0227
162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0226
165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0226
168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0225
171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0225
174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0225
177/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0225
180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0224
183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0224
186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0223
189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5620 - loss: 1.0222
192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5620 - loss: 1.0221
195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5620 - loss: 1.0220
198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0219
201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0218
204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0217
207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0216
210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0215
213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0214
216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0214
219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0213
222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0213
225/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5622 - loss: 1.0212
228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5622 - loss: 1.0212
231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5622 - loss: 1.0211
234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5621 - loss: 1.0211
237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5621 - loss: 1.0210
240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5621 - loss: 1.0209
243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5621 - loss: 1.0209
246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5621 - loss: 1.0208
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Epoch 40: val_accuracy did not improve from 0.59273
473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5620 - loss: 1.0173 - val_accuracy: 0.5917 - val_loss: 0.9724 - learning_rate: 8.0000e-05
Restoring model weights from the end of the best epoch: 37.
Plotting the Training and Validation Accuracies¶
plt.plot(history_efficient.history["accuracy"])
plt.plot(history_efficient.history["val_accuracy"])
plt.title("EfficientNet Model accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train", "validation"], loc="upper left")
plt.show()
Evaluating the EfficientnetNet Model¶
# Calculate the number of steps for the entire test set to be processed
test_steps = test_generator_efficientnet.samples // batch_size
# If the number of samples isn't a multiple of the batch size,
# you have one more batch with the remaining samples
if test_generator_efficientnet.samples % batch_size > 0:
test_steps += 1
# Evaluating the model on the test set
evaluation_results = new_efficient_model.evaluate(test_generator_efficientnet, steps=test_steps)
print(f"Loss: {evaluation_results[0]}, Accuracy: {evaluation_results[1]}")
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step - accuracy: 0.8125 - loss: 0.6692
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.6635 - loss: 0.8448
Loss: 0.8933918476104736, Accuracy: 0.625
Plotting the confusion matrix¶
pred_probabilities = new_efficient_model.predict(test_generator_efficientnet, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)
# Getting the true labels from the generator
y_true = test_generator_efficientnet.classes
# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))
# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("EfficientNet Model Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 7s 3s/step
4/4 ━━━━━━━━━━━━━━━━━━━━ 3s 5ms/step
precision recall f1-score support
happy 0.53 0.81 0.64 32
neutral 0.50 0.47 0.48 32
sad 0.78 0.56 0.65 32
surprise 0.81 0.66 0.72 32
accuracy 0.62 128
macro avg 0.66 0.62 0.63 128
weighted avg 0.66 0.62 0.63 128
Observations and Insights:
- The EfficientNetV2B0 model, with its 5,919,312 total parameters, with no model layers cut, is a balance of complexity and efficiency, suitable for our grayscale 48x48 emotion classification task.
- On the test set, the model achieved an accuracy of 62.5%.
- The confusion matrix reveals the model's strengths and weaknesses: it was most precise with 'surprise' (precision of 0.81) and 'sad' (precision of 0.78) emotions, but it struggled to correctly identify 'happy' emotions (with the highest recall at 0.81), suggesting that it often misclassifies other emotions as 'happy'.
Think About It:
- What is your overall performance of these Transfer Learning Architectures? Can we draw a comparison of these models' performances. Are we satisfied with the accuracies that we have received?
- Answer: The overall performance of the transfer learning architectures is moderately satisfactory. They are capable of learning from the dataset, but clearly there is still room for improvement. We have tested cutting the models in different layers and playing with different fully connected layers, but the result did not improve significantly. Maybe it requires to go deeper to the nature of each model to understand what changes need to be made to get the most out of them.
- Do you think our issue lies with 'rgb' color_mode?
- Answer: It may be one of the possible issues, but not the main one. I would go more on the type of images (and also size), on which these models were trained for. So that, the features learned cannot be used on our task in hand.
Now that we have tried multiple pre-trained models, let's build a complex CNN architecture and see if we can get better performance.
Building a Complex Neural Network Architecture¶
In this section, we will build a more complex Convolutional Neural Network Model that has close to as many parameters as we had in our Transfer Learning Models. However, we will have only 1 input channel for our input images.
Creating our Data Loaders¶
In this section, we are creating data loaders which we will use as inputs to the more Complicated Convolutional Neural Network. We will go ahead with color_mode = 'grayscale'.
# Set this to 'grayscale' as the images are in grayscale
color_mode = "grayscale"
color_layers = 1
# As we have checked, all images are 48x48, we will set the img_width and img_height to 48
img_width, img_height = 48, 48
# A batch size of 32 is appropriate for this dataset provide to provide a good balance
# between the model's ability to generalize (avoid overfitting) and computational efficiency.
batch_size = 32
# Training Data Augmentation
train_datagen = ImageDataGenerator(
rescale=1.0 / 255, # Normalize pixel values to [0,1]
horizontal_flip=True, # Faces are symmetric; flipping can simulate looking from another direction
brightness_range=(0.5, 1.5), # Randomly adjust brightness to simulate different lighting conditions
shear_range=0.3, # Shear transformations for perspective changes
rotation_range=20, # Slight rotation to introduce variability without distorting emotion features
width_shift_range=0.1, # Slight horizontal shifts to simulate off-center faces
height_shift_range=0.1, # Slight vertical shifts to account for different heights/angles
zoom_range=0.1, # Small zoom in/out to simulate closer or further away faces
)
# Validation and Testing Data should not be augmented!
validation_datagen = ImageDataGenerator(rescale=1.0 / 255)
test_datagen = ImageDataGenerator(rescale=1.0 / 255)
# Creating train_dir, validation_dir, and test_dir with the structure of DATADIR and SUBDIRS
train_dir = os.path.join(DATADIR, SUBDIRS_DICT["train"])
validation_dir = os.path.join(DATADIR, SUBDIRS_DICT["validation"])
test_dir = os.path.join(DATADIR, SUBDIRS_DICT["test"])
# Train Generator
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode, # Set to 'grayscale'
class_mode="categorical",
)
# Validation Generator
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode, # Set to 'grayscale'
class_mode="categorical",
shuffle=False, # shuffle=False to keep data in order for evaluation
)
# Testing Generator
test_generator = test_datagen.flow_from_directory(
test_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
color_mode=color_mode, # Set to 'grayscale'
class_mode="categorical",
shuffle=False, # shuffle=False to keep data in order for testing
)
Found 15109 images belonging to 4 classes.
Found 4977 images belonging to 4 classes.
Found 128 images belonging to 4 classes.
Model Building¶
- Try building a layer with 5 Convolutional Blocks and see if performance increases.
backend.clear_session()
# Fixing the seed for random number generators so that we can ensure we receive the same output everytime
np.random.seed(42)
random.seed(42)
tf.random.set_seed(42)
# Initializing a sequential model
model_complex = Sequential()
model_complex.add(Input(shape=(img_width, img_height, color_layers)))
# First Convolutional Block
model_complex.add(Conv2D(64, kernel_size=2, padding="same"))
model_complex.add(BatchNormalization())
model_complex.add(LeakyReLU(negative_slope=0.1))
model_complex.add(MaxPooling2D(pool_size=2))
# Second Convolutional Block
model_complex.add(Conv2D(128, kernel_size=2, padding="same"))
model_complex.add(BatchNormalization())
model_complex.add(LeakyReLU(negative_slope=0.1))
model_complex.add(MaxPooling2D(pool_size=2))
# Third Convolutional Block
model_complex.add(Conv2D(256, kernel_size=2, padding="same"))
model_complex.add(BatchNormalization())
model_complex.add(LeakyReLU(negative_slope=0.1))
model_complex.add(MaxPooling2D(pool_size=2))
# Fourth Convolutional Block
model_complex.add(Conv2D(512, kernel_size=2, padding="same"))
model_complex.add(BatchNormalization())
model_complex.add(LeakyReLU(negative_slope=0.1))
model_complex.add(MaxPooling2D(pool_size=2))
# Fifth Convolutional Block
model_complex.add(Conv2D(128, kernel_size=2, padding="same"))
model_complex.add(BatchNormalization())
model_complex.add(LeakyReLU(negative_slope=0.1))
model_complex.add(MaxPooling2D(pool_size=2))
# Flatten the output of the conv layers to feed into the dense layers
model_complex.add(Flatten())
model_complex.add(Dense(512, activation="relu"))
model_complex.add(Dense(128, activation="relu"))
model_complex.add(Dense(64))
model_complex.add(BatchNormalization())
model_complex.add(ReLU()) # Using ReLU after batch normalization
model_complex.add(Dense(4, activation="softmax"))
# Using RMSProp Optimizer
optimizer = RMSprop(learning_rate=0.01)
Compiling and Training the Model¶
model_complex.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])
model_complex.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ conv2d (Conv2D) │ (None, 48, 48, 64) │ 320 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization │ (None, 48, 48, 64) │ 256 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ leaky_re_lu (LeakyReLU) │ (None, 48, 48, 64) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d (MaxPooling2D) │ (None, 24, 24, 64) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_1 (Conv2D) │ (None, 24, 24, 128) │ 32,896 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization_1 │ (None, 24, 24, 128) │ 512 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ leaky_re_lu_1 (LeakyReLU) │ (None, 24, 24, 128) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_1 (MaxPooling2D) │ (None, 12, 12, 128) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_2 (Conv2D) │ (None, 12, 12, 256) │ 131,328 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization_2 │ (None, 12, 12, 256) │ 1,024 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ leaky_re_lu_2 (LeakyReLU) │ (None, 12, 12, 256) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_2 (MaxPooling2D) │ (None, 6, 6, 256) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_3 (Conv2D) │ (None, 6, 6, 512) │ 524,800 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization_3 │ (None, 6, 6, 512) │ 2,048 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ leaky_re_lu_3 (LeakyReLU) │ (None, 6, 6, 512) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_3 (MaxPooling2D) │ (None, 3, 3, 512) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_4 (Conv2D) │ (None, 3, 3, 128) │ 262,272 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization_4 │ (None, 3, 3, 128) │ 512 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ leaky_re_lu_4 (LeakyReLU) │ (None, 3, 3, 128) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_4 (MaxPooling2D) │ (None, 1, 1, 128) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ flatten (Flatten) │ (None, 128) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense (Dense) │ (None, 512) │ 66,048 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_1 (Dense) │ (None, 128) │ 65,664 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_2 (Dense) │ (None, 64) │ 8,256 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization_5 │ (None, 64) │ 256 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ re_lu (ReLU) │ (None, 64) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_3 (Dense) │ (None, 4) │ 260 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 1,096,452 (4.18 MB)
Trainable params: 1,094,148 (4.17 MB)
Non-trainable params: 2,304 (9.00 KB)
# Get the current time
current_time = datetime.now().strftime("%Y%m%d-%H%M%S")
# Set up Early Stopping with a patience 7 but acting after at least 30 epochs
delayed_early_stopping = DelayedEarlyStopping(
monitor="val_loss", patience=7, verbose=1, restore_best_weights=True, start_epoch=30
)
# Define the learning rate scheduler callback
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, patience=5, min_lr=0.00001, verbose=1)
# Define the saving the best model callback
mc = ModelCheckpoint(
f"{results_path}/best_model_complex_{current_time}.keras",
monitor="val_accuracy",
mode="max",
verbose=1,
save_best_only=True,
)
# Fitting the model with 60 epochs and using validation set
history_complex = model_complex.fit(
train_generator,
epochs=60,
validation_data=validation_generator,
callbacks=[reduce_lr, mc, delayed_early_stopping],
)
Epoch 1/60
/home/iamtxena/sandbox/mit-ai/my_env/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored. self._warn_if_super_not_called()
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5/473 ━━━━━━━━━━━━━━━━━━━━ 7s 16ms/step - accuracy: 0.2273 - loss: 1.7262
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Epoch 1: val_accuracy improved from -inf to 0.44967, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 13s 20ms/step - accuracy: 0.2916 - loss: 1.4084 - val_accuracy: 0.4497 - val_loss: 1.2479 - learning_rate: 0.0100
Epoch 2/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.3125 - loss: 1.2842
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365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.4329 - loss: 1.2020
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438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.4367 - loss: 1.1959
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.4369 - loss: 1.1954
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.4372 - loss: 1.1950
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.4375 - loss: 1.1945
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.4378 - loss: 1.1941
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.4381 - loss: 1.1936
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.4384 - loss: 1.1930
Epoch 2: val_accuracy improved from 0.44967 to 0.54872, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.4387 - loss: 1.1926 - val_accuracy: 0.5487 - val_loss: 1.0995 - learning_rate: 0.0100
Epoch 3/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 51s 109ms/step - accuracy: 0.5938 - loss: 0.9162
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.5681 - loss: 1.0240
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5431 - loss: 1.0657
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Epoch 3: val_accuracy improved from 0.54872 to 0.57304, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 7s 14ms/step - accuracy: 0.5218 - loss: 1.0473 - val_accuracy: 0.5730 - val_loss: 0.9766 - learning_rate: 0.0100
Epoch 4/60
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Epoch 4: val_accuracy improved from 0.57304 to 0.58891, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5627 - loss: 0.9599 - val_accuracy: 0.5889 - val_loss: 0.9360 - learning_rate: 0.0100
Epoch 5/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 55s 118ms/step - accuracy: 0.6250 - loss: 0.7415
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290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5705 - loss: 0.9314
295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5708 - loss: 0.9311
300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5710 - loss: 0.9308
305/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5712 - loss: 0.9306
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5714 - loss: 0.9303
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5716 - loss: 0.9301
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5717 - loss: 0.9299
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5719 - loss: 0.9296
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5721 - loss: 0.9294
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5723 - loss: 0.9292
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5724 - loss: 0.9290
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5726 - loss: 0.9287
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5727 - loss: 0.9286
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5729 - loss: 0.9284
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5730 - loss: 0.9282
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5731 - loss: 0.9280
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5733 - loss: 0.9279
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5734 - loss: 0.9277
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5736 - loss: 0.9276
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5737 - loss: 0.9274
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5738 - loss: 0.9272
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5740 - loss: 0.9271
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5741 - loss: 0.9269
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5743 - loss: 0.9267
411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5744 - loss: 0.9266
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5745 - loss: 0.9264
421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5747 - loss: 0.9263
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5748 - loss: 0.9261
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5749 - loss: 0.9260
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5750 - loss: 0.9259
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5751 - loss: 0.9257
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5753 - loss: 0.9256
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5754 - loss: 0.9255
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5755 - loss: 0.9253
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5756 - loss: 0.9252
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5757 - loss: 0.9251
Epoch 5: val_accuracy did not improve from 0.58891
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5759 - loss: 0.9249 - val_accuracy: 0.3789 - val_loss: 1.5197 - learning_rate: 0.0100
Epoch 6/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.5000 - loss: 0.9217
4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5671 - loss: 0.8888
9/473 ━━━━━━━━━━━━━━━━━━━━ 7s 15ms/step - accuracy: 0.5732 - loss: 0.8870
13/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.5757 - loss: 0.8889
17/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.5775 - loss: 0.8906
22/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.5808 - loss: 0.8888
27/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.5837 - loss: 0.8871
32/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.5856 - loss: 0.8874
37/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.5868 - loss: 0.8878
42/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.5873 - loss: 0.8880
47/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5874 - loss: 0.8875
52/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5882 - loss: 0.8859
57/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5889 - loss: 0.8842
62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5894 - loss: 0.8826
67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5897 - loss: 0.8813
72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5901 - loss: 0.8800
77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5907 - loss: 0.8787
82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5913 - loss: 0.8777
87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5918 - loss: 0.8772
92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5922 - loss: 0.8768
97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5927 - loss: 0.8765
102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5931 - loss: 0.8763
107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5933 - loss: 0.8763
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5935 - loss: 0.8764
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5937 - loss: 0.8764
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5939 - loss: 0.8763
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5941 - loss: 0.8762
127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5942 - loss: 0.8762
132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5943 - loss: 0.8763
137/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5945 - loss: 0.8764
141/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5946 - loss: 0.8766
146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5948 - loss: 0.8768
151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5949 - loss: 0.8770
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5950 - loss: 0.8771
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5952 - loss: 0.8773
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5954 - loss: 0.8775
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5956 - loss: 0.8775
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5958 - loss: 0.8775
178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5960 - loss: 0.8774
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5963 - loss: 0.8774
188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5965 - loss: 0.8773
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5967 - loss: 0.8773
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5969 - loss: 0.8774
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5971 - loss: 0.8775
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5972 - loss: 0.8776
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5973 - loss: 0.8777
218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5974 - loss: 0.8779
223/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5976 - loss: 0.8781
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5977 - loss: 0.8783
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5978 - loss: 0.8784
237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5980 - loss: 0.8785
242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5981 - loss: 0.8786
247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5982 - loss: 0.8787
252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5983 - loss: 0.8788
257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5984 - loss: 0.8789
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5985 - loss: 0.8790
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5986 - loss: 0.8791
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5987 - loss: 0.8792
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5988 - loss: 0.8792
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5989 - loss: 0.8793
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5990 - loss: 0.8793
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5991 - loss: 0.8794
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5992 - loss: 0.8795
302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5993 - loss: 0.8795
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5994 - loss: 0.8796
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5995 - loss: 0.8797
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5995 - loss: 0.8797
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5996 - loss: 0.8798
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5997 - loss: 0.8799
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5998 - loss: 0.8799
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5999 - loss: 0.8800
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6000 - loss: 0.8800
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6001 - loss: 0.8801
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6002 - loss: 0.8801
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6002 - loss: 0.8802
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6003 - loss: 0.8802
367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6004 - loss: 0.8803
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6005 - loss: 0.8803
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6006 - loss: 0.8804
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6007 - loss: 0.8804
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6007 - loss: 0.8805
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6008 - loss: 0.8805
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6009 - loss: 0.8806
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6010 - loss: 0.8807
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6010 - loss: 0.8808
411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6011 - loss: 0.8809
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6012 - loss: 0.8810
421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6012 - loss: 0.8811
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Epoch 6: val_accuracy improved from 0.58891 to 0.62829, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6018 - loss: 0.8820 - val_accuracy: 0.6283 - val_loss: 0.9162 - learning_rate: 0.0100
Epoch 7/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.5625 - loss: 0.8330
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340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6102 - loss: 0.8737
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439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6127 - loss: 0.8727
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6128 - loss: 0.8726
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6129 - loss: 0.8726
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6131 - loss: 0.8725
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6132 - loss: 0.8725
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6133 - loss: 0.8724
467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6134 - loss: 0.8724
Epoch 7: val_accuracy improved from 0.62829 to 0.69761, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6136 - loss: 0.8723 - val_accuracy: 0.6976 - val_loss: 0.7406 - learning_rate: 0.0100
Epoch 8/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 52s 112ms/step - accuracy: 0.7500 - loss: 0.7680
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7393 - loss: 0.7492
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7177 - loss: 0.7629
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59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6775 - loss: 0.8029
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6760 - loss: 0.8035
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6751 - loss: 0.8039
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6740 - loss: 0.8048
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6729 - loss: 0.8056
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6719 - loss: 0.8063
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6709 - loss: 0.8070
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6699 - loss: 0.8079
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6689 - loss: 0.8088
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6680 - loss: 0.8096
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6672 - loss: 0.8104
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6666 - loss: 0.8109
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6660 - loss: 0.8115
125/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6655 - loss: 0.8120
130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6650 - loss: 0.8124
135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6646 - loss: 0.8127
140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6642 - loss: 0.8131
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6639 - loss: 0.8135
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6635 - loss: 0.8139
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6631 - loss: 0.8144
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6626 - loss: 0.8149
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6622 - loss: 0.8153
168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6618 - loss: 0.8157
172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6614 - loss: 0.8161
177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6610 - loss: 0.8165
181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6607 - loss: 0.8168
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6603 - loss: 0.8172
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6600 - loss: 0.8175
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6597 - loss: 0.8178
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6594 - loss: 0.8181
201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6592 - loss: 0.8183
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6588 - loss: 0.8187
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6585 - loss: 0.8191
216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6581 - loss: 0.8194
221/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6578 - loss: 0.8197
226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6576 - loss: 0.8201
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6573 - loss: 0.8204
235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6571 - loss: 0.8206
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6568 - loss: 0.8209
245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6566 - loss: 0.8213
250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6563 - loss: 0.8216
255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6561 - loss: 0.8218
260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6559 - loss: 0.8221
265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6557 - loss: 0.8223
270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6556 - loss: 0.8226
275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6554 - loss: 0.8228
280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6552 - loss: 0.8230
285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6551 - loss: 0.8232
290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6550 - loss: 0.8235
295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6548 - loss: 0.8237
300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6547 - loss: 0.8239
305/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6546 - loss: 0.8241
310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6544 - loss: 0.8243
315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6543 - loss: 0.8244
320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6542 - loss: 0.8246
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6541 - loss: 0.8247
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6541 - loss: 0.8249
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6540 - loss: 0.8250
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6539 - loss: 0.8251
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6539 - loss: 0.8251
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6539 - loss: 0.8252
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6538 - loss: 0.8253
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6538 - loss: 0.8253
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6537 - loss: 0.8254
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6537 - loss: 0.8255
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6537 - loss: 0.8255
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6536 - loss: 0.8256
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6536 - loss: 0.8257
390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6536 - loss: 0.8258
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6535 - loss: 0.8258
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6535 - loss: 0.8259
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6535 - loss: 0.8260
410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6535 - loss: 0.8260
415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8261
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8261
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8261
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8263
Epoch 8: val_accuracy did not improve from 0.69761
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6534 - loss: 0.8263 - val_accuracy: 0.5724 - val_loss: 1.0251 - learning_rate: 0.0100
Epoch 9/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.7188 - loss: 0.6935
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6993 - loss: 0.7490
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6895 - loss: 0.7740
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6858 - loss: 0.7805
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6834 - loss: 0.7863
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6819 - loss: 0.7912
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6802 - loss: 0.7974
36/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6787 - loss: 0.8011
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6782 - loss: 0.8020
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6775 - loss: 0.8021
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6768 - loss: 0.8021
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6761 - loss: 0.8020
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6756 - loss: 0.8022
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6751 - loss: 0.8026
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6745 - loss: 0.8031
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6742 - loss: 0.8034
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6738 - loss: 0.8038
86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6734 - loss: 0.8041
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6732 - loss: 0.8042
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6730 - loss: 0.8040
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6729 - loss: 0.8038
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6728 - loss: 0.8036
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6727 - loss: 0.8035
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6725 - loss: 0.8033
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6724 - loss: 0.8031
126/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6722 - loss: 0.8029
131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8026
136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8023
141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8019
146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8015
151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8012
156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8008
161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6720 - loss: 0.8006
166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6720 - loss: 0.8004
171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6719 - loss: 0.8002
176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6719 - loss: 0.8001
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6718 - loss: 0.8000
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6717 - loss: 0.8000
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6715 - loss: 0.8000
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6714 - loss: 0.8000
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6713 - loss: 0.8000
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6711 - loss: 0.8001
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6710 - loss: 0.8003
214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6708 - loss: 0.8004
218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6708 - loss: 0.8005
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6707 - loss: 0.8006
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6706 - loss: 0.8006
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6706 - loss: 0.8006
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6705 - loss: 0.8006
242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6705 - loss: 0.8006
247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6705 - loss: 0.8006
250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6705 - loss: 0.8006
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6705 - loss: 0.8006
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6704 - loss: 0.8006
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6704 - loss: 0.8006
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6704 - loss: 0.8006
274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6703 - loss: 0.8007
279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6703 - loss: 0.8007
284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6702 - loss: 0.8008
289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6702 - loss: 0.8008
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6701 - loss: 0.8009
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6701 - loss: 0.8009
303/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6700 - loss: 0.8010
308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6699 - loss: 0.8011
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6699 - loss: 0.8011
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6698 - loss: 0.8012
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6697 - loss: 0.8012
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6697 - loss: 0.8013
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6696 - loss: 0.8014
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6695 - loss: 0.8014
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6694 - loss: 0.8015
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6694 - loss: 0.8015
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6693 - loss: 0.8015
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6693 - loss: 0.8015
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6692 - loss: 0.8015
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6692 - loss: 0.8015
367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6691 - loss: 0.8016
372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6690 - loss: 0.8016
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6689 - loss: 0.8017
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6689 - loss: 0.8017
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6688 - loss: 0.8018
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6687 - loss: 0.8019
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6687 - loss: 0.8019
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6686 - loss: 0.8019
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6685 - loss: 0.8020
410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6685 - loss: 0.8020
415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6684 - loss: 0.8021
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6683 - loss: 0.8022
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6683 - loss: 0.8022
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6682 - loss: 0.8023
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6682 - loss: 0.8023
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6681 - loss: 0.8024
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6681 - loss: 0.8024
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6680 - loss: 0.8025
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6680 - loss: 0.8025
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6679 - loss: 0.8026
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6679 - loss: 0.8026
469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6678 - loss: 0.8027
Epoch 9: val_accuracy did not improve from 0.69761
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6678 - loss: 0.8027 - val_accuracy: 0.6416 - val_loss: 0.8362 - learning_rate: 0.0100
Epoch 10/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.5625 - loss: 0.9427
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.5990 - loss: 0.9089
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6241 - loss: 0.8765
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6355 - loss: 0.8574
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6447 - loss: 0.8410
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6532 - loss: 0.8264
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6581 - loss: 0.8185
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6598 - loss: 0.8161
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6614 - loss: 0.8141
46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6626 - loss: 0.8130
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6636 - loss: 0.8118
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6647 - loss: 0.8103
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6654 - loss: 0.8092
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6661 - loss: 0.8082
72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6672 - loss: 0.8072
77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6679 - loss: 0.8066
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6683 - loss: 0.8062
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6689 - loss: 0.8056
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6694 - loss: 0.8050
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6700 - loss: 0.8044
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6703 - loss: 0.8039
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6706 - loss: 0.8035
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6710 - loss: 0.8030
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6713 - loss: 0.8023
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6717 - loss: 0.8017
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6720 - loss: 0.8011
129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6724 - loss: 0.8005
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6729 - loss: 0.7997
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6733 - loss: 0.7990
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6736 - loss: 0.7984
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6739 - loss: 0.7978
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6741 - loss: 0.7973
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6743 - loss: 0.7968
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6745 - loss: 0.7964
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6746 - loss: 0.7959
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6748 - loss: 0.7956
178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6749 - loss: 0.7953
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6750 - loss: 0.7950
188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6751 - loss: 0.7946
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6752 - loss: 0.7943
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6753 - loss: 0.7940
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6754 - loss: 0.7937
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6755 - loss: 0.7934
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6756 - loss: 0.7930
218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6757 - loss: 0.7927
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6757 - loss: 0.7924
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6758 - loss: 0.7921
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6759 - loss: 0.7918
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6759 - loss: 0.7914
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6760 - loss: 0.7911
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6761 - loss: 0.7907
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6762 - loss: 0.7904
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6763 - loss: 0.7900
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6764 - loss: 0.7897
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6765 - loss: 0.7895
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6766 - loss: 0.7893
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6767 - loss: 0.7891
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6767 - loss: 0.7889
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6768 - loss: 0.7887
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6768 - loss: 0.7885
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6769 - loss: 0.7883
303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6769 - loss: 0.7882
308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6770 - loss: 0.7881
313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6770 - loss: 0.7879
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6771 - loss: 0.7877
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6771 - loss: 0.7876
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6772 - loss: 0.7875
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6772 - loss: 0.7874
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6772 - loss: 0.7873
343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6772 - loss: 0.7872
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6772 - loss: 0.7871
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7870
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7869
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7869
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7868
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7867
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7867
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7866
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7865
390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7864
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7864
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7863
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7863
410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7862
415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7861
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7861
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7860
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7860
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7859
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7858
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7858
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7857
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7857
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7856
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7855
Epoch 10: val_accuracy did not improve from 0.69761
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6773 - loss: 0.7855 - val_accuracy: 0.6448 - val_loss: 0.8493 - learning_rate: 0.0100
Epoch 11/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.6562 - loss: 0.7888
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6807 - loss: 0.7486
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6873 - loss: 0.7464
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6925 - loss: 0.7370
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6945 - loss: 0.7330
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6959 - loss: 0.7304
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6969 - loss: 0.7295
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6972 - loss: 0.7301
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6973 - loss: 0.7323
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6967 - loss: 0.7352
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6956 - loss: 0.7387
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6945 - loss: 0.7413
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6937 - loss: 0.7429
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6934 - loss: 0.7435
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6930 - loss: 0.7441
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6926 - loss: 0.7447
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6920 - loss: 0.7458
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6914 - loss: 0.7466
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6907 - loss: 0.7476
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6901 - loss: 0.7484
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6897 - loss: 0.7490
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6894 - loss: 0.7496
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6892 - loss: 0.7502
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6889 - loss: 0.7506
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6887 - loss: 0.7511
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6884 - loss: 0.7516
129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6882 - loss: 0.7522
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6880 - loss: 0.7528
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6877 - loss: 0.7534
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6875 - loss: 0.7540
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6872 - loss: 0.7546
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6870 - loss: 0.7551
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6868 - loss: 0.7557
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6866 - loss: 0.7563
168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6865 - loss: 0.7567
173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6864 - loss: 0.7572
177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6862 - loss: 0.7575
181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6861 - loss: 0.7579
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6859 - loss: 0.7582
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6858 - loss: 0.7585
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6857 - loss: 0.7588
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6856 - loss: 0.7592
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6855 - loss: 0.7595
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6854 - loss: 0.7598
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6853 - loss: 0.7601
218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6852 - loss: 0.7605
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6851 - loss: 0.7608
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6850 - loss: 0.7611
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6849 - loss: 0.7614
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6849 - loss: 0.7617
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6848 - loss: 0.7619
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6848 - loss: 0.7622
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6847 - loss: 0.7625
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6847 - loss: 0.7627
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6846 - loss: 0.7630
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6846 - loss: 0.7632
274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6845 - loss: 0.7634
279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6845 - loss: 0.7636
284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6844 - loss: 0.7638
289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6843 - loss: 0.7641
294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6842 - loss: 0.7643
299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6842 - loss: 0.7645
304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6841 - loss: 0.7646
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6840 - loss: 0.7648
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6839 - loss: 0.7650
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6838 - loss: 0.7652
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6838 - loss: 0.7653
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6837 - loss: 0.7655
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6836 - loss: 0.7656
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6836 - loss: 0.7658
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6835 - loss: 0.7659
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6835 - loss: 0.7661
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6835 - loss: 0.7662
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6834 - loss: 0.7663
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6834 - loss: 0.7664
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6834 - loss: 0.7665
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6833 - loss: 0.7666
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6833 - loss: 0.7667
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6832 - loss: 0.7669
389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6832 - loss: 0.7670
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6832 - loss: 0.7671
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6831 - loss: 0.7672
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6831 - loss: 0.7673
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6831 - loss: 0.7674
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6830 - loss: 0.7675
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6830 - loss: 0.7676
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6829 - loss: 0.7677
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6829 - loss: 0.7678
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6828 - loss: 0.7679
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6828 - loss: 0.7680
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6828 - loss: 0.7681
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6827 - loss: 0.7682
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6827 - loss: 0.7682
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6827 - loss: 0.7683
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465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6827 - loss: 0.7684
Epoch 11: val_accuracy did not improve from 0.69761
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6826 - loss: 0.7685 - val_accuracy: 0.6446 - val_loss: 0.8730 - learning_rate: 0.0100
Epoch 12/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.8125 - loss: 0.5180
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7330 - loss: 0.7489
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7024 - loss: 0.7935
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6974 - loss: 0.7869
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6930 - loss: 0.7850
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28/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6860 - loss: 0.7865
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37/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6819 - loss: 0.7850
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81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6800 - loss: 0.7806
86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6802 - loss: 0.7799
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6803 - loss: 0.7794
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6806 - loss: 0.7787
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6808 - loss: 0.7779
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6811 - loss: 0.7772
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127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6821 - loss: 0.7748
132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6821 - loss: 0.7746
137/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6823 - loss: 0.7743
142/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6825 - loss: 0.7739
147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6827 - loss: 0.7736
152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6829 - loss: 0.7734
157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6830 - loss: 0.7732
162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6831 - loss: 0.7731
167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6831 - loss: 0.7730
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187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6832 - loss: 0.7727
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210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6833 - loss: 0.7731
215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6833 - loss: 0.7732
220/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6834 - loss: 0.7733
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6834 - loss: 0.7733
230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6835 - loss: 0.7733
235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6836 - loss: 0.7733
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245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6836 - loss: 0.7734
250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6837 - loss: 0.7734
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270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6839 - loss: 0.7733
275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6840 - loss: 0.7733
280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6840 - loss: 0.7732
285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6841 - loss: 0.7732
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295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6841 - loss: 0.7731
300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6841 - loss: 0.7730
305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6842 - loss: 0.7730
310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6842 - loss: 0.7729
315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6842 - loss: 0.7729
320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6843 - loss: 0.7728
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6843 - loss: 0.7728
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6843 - loss: 0.7727
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6844 - loss: 0.7727
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6844 - loss: 0.7726
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6844 - loss: 0.7726
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6845 - loss: 0.7725
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6845 - loss: 0.7724
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6845 - loss: 0.7724
363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6845 - loss: 0.7724
368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6845 - loss: 0.7723
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382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6845 - loss: 0.7723
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6845 - loss: 0.7722
390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6845 - loss: 0.7722
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6846 - loss: 0.7721
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6846 - loss: 0.7721
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6846 - loss: 0.7720
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6846 - loss: 0.7720
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429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6848 - loss: 0.7717
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6848 - loss: 0.7716
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6848 - loss: 0.7715
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6848 - loss: 0.7715
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6849 - loss: 0.7714
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6849 - loss: 0.7713
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6849 - loss: 0.7713
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6849 - loss: 0.7712
473/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6850 - loss: 0.7711
Epoch 12: val_accuracy improved from 0.69761 to 0.71529, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6850 - loss: 0.7711 - val_accuracy: 0.7153 - val_loss: 0.7055 - learning_rate: 0.0100
Epoch 13/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 107ms/step - accuracy: 0.5000 - loss: 0.9968
5/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6178 - loss: 0.8463
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6465 - loss: 0.7966
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6530 - loss: 0.7883
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6606 - loss: 0.7823
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6665 - loss: 0.7791
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45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6787 - loss: 0.7652
49/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6800 - loss: 0.7643
54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6816 - loss: 0.7631
59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6824 - loss: 0.7627
64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6829 - loss: 0.7623
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6835 - loss: 0.7616
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6839 - loss: 0.7607
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6841 - loss: 0.7601
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6843 - loss: 0.7595
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6847 - loss: 0.7589
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6852 - loss: 0.7583
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6856 - loss: 0.7580
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6859 - loss: 0.7579
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6861 - loss: 0.7580
113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6863 - loss: 0.7581
118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6865 - loss: 0.7582
123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6867 - loss: 0.7582
128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6868 - loss: 0.7583
133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6869 - loss: 0.7584
138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6871 - loss: 0.7583
143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6872 - loss: 0.7583
148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6874 - loss: 0.7585
151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6874 - loss: 0.7585
156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6875 - loss: 0.7586
161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6877 - loss: 0.7586
166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6878 - loss: 0.7586
171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6879 - loss: 0.7587
176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6879 - loss: 0.7588
181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6880 - loss: 0.7589
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6880 - loss: 0.7590
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6880 - loss: 0.7591
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6880 - loss: 0.7592
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6881 - loss: 0.7593
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6881 - loss: 0.7594
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6882 - loss: 0.7594
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6883 - loss: 0.7594
218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6884 - loss: 0.7594
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6886 - loss: 0.7593
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6887 - loss: 0.7593
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6887 - loss: 0.7592
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6888 - loss: 0.7592
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6889 - loss: 0.7591
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6890 - loss: 0.7590
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6890 - loss: 0.7589
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6891 - loss: 0.7588
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6892 - loss: 0.7587
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6893 - loss: 0.7586
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6893 - loss: 0.7585
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6894 - loss: 0.7584
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6894 - loss: 0.7583
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6894 - loss: 0.7581
290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6895 - loss: 0.7580
295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6895 - loss: 0.7579
300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6896 - loss: 0.7578
305/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6896 - loss: 0.7577
310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6897 - loss: 0.7576
315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6897 - loss: 0.7575
320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6898 - loss: 0.7574
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6898 - loss: 0.7573
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6898 - loss: 0.7573
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6899 - loss: 0.7572
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6899 - loss: 0.7571
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6900 - loss: 0.7570
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6900 - loss: 0.7569
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6900 - loss: 0.7569
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6901 - loss: 0.7568
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6901 - loss: 0.7567
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6901 - loss: 0.7567
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6902 - loss: 0.7566
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6902 - loss: 0.7566
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6902 - loss: 0.7565
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6903 - loss: 0.7565
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6903 - loss: 0.7564
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6904 - loss: 0.7563
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6904 - loss: 0.7562
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6905 - loss: 0.7561
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6905 - loss: 0.7561
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6905 - loss: 0.7560
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6906 - loss: 0.7559
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6906 - loss: 0.7558
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6907 - loss: 0.7557
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6907 - loss: 0.7556
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6908 - loss: 0.7555
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6909 - loss: 0.7555
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6909 - loss: 0.7554
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6910 - loss: 0.7553
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6910 - loss: 0.7552
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6911 - loss: 0.7551
Epoch 13: val_accuracy did not improve from 0.71529
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6911 - loss: 0.7551 - val_accuracy: 0.5863 - val_loss: 0.9923 - learning_rate: 0.0100
Epoch 14/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.8438 - loss: 0.6637
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7689 - loss: 0.7116
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7272 - loss: 0.7679
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7093 - loss: 0.7884
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6992 - loss: 0.7956
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6937 - loss: 0.7944
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6901 - loss: 0.7922
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6884 - loss: 0.7891
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6872 - loss: 0.7864
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6870 - loss: 0.7824
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6874 - loss: 0.7781
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6876 - loss: 0.7750
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6878 - loss: 0.7727
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6882 - loss: 0.7707
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6885 - loss: 0.7689
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6888 - loss: 0.7675
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6891 - loss: 0.7660
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6896 - loss: 0.7645
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6902 - loss: 0.7630
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6905 - loss: 0.7623
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6908 - loss: 0.7616
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6910 - loss: 0.7611
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6911 - loss: 0.7608
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6912 - loss: 0.7604
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6912 - loss: 0.7600
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6913 - loss: 0.7595
129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6913 - loss: 0.7592
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6913 - loss: 0.7587
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6914 - loss: 0.7583
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6914 - loss: 0.7578
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6915 - loss: 0.7575
152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6915 - loss: 0.7572
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6916 - loss: 0.7570
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6916 - loss: 0.7566
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6917 - loss: 0.7562
168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6917 - loss: 0.7560
173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6918 - loss: 0.7556
178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6919 - loss: 0.7550
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6921 - loss: 0.7544
187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6922 - loss: 0.7540
192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6924 - loss: 0.7535
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Epoch 14: val_accuracy improved from 0.71529 to 0.73157, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6952 - loss: 0.7444 - val_accuracy: 0.7316 - val_loss: 0.6710 - learning_rate: 0.0100
Epoch 15/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.6562 - loss: 0.7261
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315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7063 - loss: 0.7118
320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7062 - loss: 0.7119
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7061 - loss: 0.7121
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7061 - loss: 0.7123
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7060 - loss: 0.7125
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7060 - loss: 0.7126
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7059 - loss: 0.7128
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7058 - loss: 0.7130
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7058 - loss: 0.7131
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7057 - loss: 0.7133
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7057 - loss: 0.7135
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7057 - loss: 0.7137
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7056 - loss: 0.7138
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7056 - loss: 0.7140
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7055 - loss: 0.7141
390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7055 - loss: 0.7143
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7055 - loss: 0.7144
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7145
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7146
410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7147
415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7148
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7149
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7150
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7150
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7151
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7152
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7153
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7154
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7155
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7053 - loss: 0.7156
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7053 - loss: 0.7156
Epoch 15: val_accuracy did not improve from 0.73157
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7053 - loss: 0.7157 - val_accuracy: 0.7135 - val_loss: 0.7173 - learning_rate: 0.0100
Epoch 16/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.5625 - loss: 1.0065
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6408 - loss: 0.8431
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6586 - loss: 0.8039
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6674 - loss: 0.7866
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6720 - loss: 0.7740
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6740 - loss: 0.7678
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6754 - loss: 0.7644
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6768 - loss: 0.7624
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6778 - loss: 0.7606
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6790 - loss: 0.7590
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6804 - loss: 0.7581
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6819 - loss: 0.7574
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6837 - loss: 0.7558
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6856 - loss: 0.7539
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6874 - loss: 0.7519
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6890 - loss: 0.7498
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6903 - loss: 0.7480
86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6914 - loss: 0.7467
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6924 - loss: 0.7454
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6933 - loss: 0.7442
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6942 - loss: 0.7429
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6951 - loss: 0.7417
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6958 - loss: 0.7406
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6964 - loss: 0.7396
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6970 - loss: 0.7386
126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6975 - loss: 0.7375
131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6979 - loss: 0.7367
136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6983 - loss: 0.7357
141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6987 - loss: 0.7350
146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6990 - loss: 0.7342
151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6993 - loss: 0.7335
156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6997 - loss: 0.7329
161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7000 - loss: 0.7323
166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7002 - loss: 0.7318
171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7004 - loss: 0.7314
176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7005 - loss: 0.7311
181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7007 - loss: 0.7308
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7008 - loss: 0.7306
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7009 - loss: 0.7304
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7010 - loss: 0.7302
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7011 - loss: 0.7300
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7013 - loss: 0.7297
207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7014 - loss: 0.7295
212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7015 - loss: 0.7292
217/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7017 - loss: 0.7289
222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7018 - loss: 0.7287
227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7019 - loss: 0.7284
232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7020 - loss: 0.7282
237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7021 - loss: 0.7279
242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7023 - loss: 0.7276
247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7024 - loss: 0.7274
252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7025 - loss: 0.7271
257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7027 - loss: 0.7268
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7028 - loss: 0.7266
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7029 - loss: 0.7264
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7030 - loss: 0.7262
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7031 - loss: 0.7260
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7032 - loss: 0.7258
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7033 - loss: 0.7257
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7033 - loss: 0.7255
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7034 - loss: 0.7254
302/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7035 - loss: 0.7253
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7036 - loss: 0.7251
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7036 - loss: 0.7250
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7037 - loss: 0.7249
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7038 - loss: 0.7247
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7038 - loss: 0.7246
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7039 - loss: 0.7245
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7040 - loss: 0.7244
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7040 - loss: 0.7242
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7041 - loss: 0.7241
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7041 - loss: 0.7240
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7042 - loss: 0.7239
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7043 - loss: 0.7238
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7043 - loss: 0.7237
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7044 - loss: 0.7236
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7044 - loss: 0.7235
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7045 - loss: 0.7233
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7045 - loss: 0.7232
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7046 - loss: 0.7232
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7046 - loss: 0.7231
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7047 - loss: 0.7230
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7047 - loss: 0.7229
411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7048 - loss: 0.7228
417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7048 - loss: 0.7227
422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7049 - loss: 0.7226
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7049 - loss: 0.7226
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7049 - loss: 0.7225
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7049 - loss: 0.7224
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7050 - loss: 0.7224
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7050 - loss: 0.7224
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7050 - loss: 0.7224
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7050 - loss: 0.7224
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7050 - loss: 0.7224
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7050 - loss: 0.7224
Epoch 16: val_accuracy did not improve from 0.73157
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7050 - loss: 0.7224 - val_accuracy: 0.6954 - val_loss: 0.7775 - learning_rate: 0.0100
Epoch 17/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:12 154ms/step - accuracy: 0.5938 - loss: 0.8235
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6789 - loss: 0.7218
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6890 - loss: 0.7009
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6953 - loss: 0.6908
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6992 - loss: 0.6854
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7025 - loss: 0.6832
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7041 - loss: 0.6839
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7055 - loss: 0.6838
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7061 - loss: 0.6840
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51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7066 - loss: 0.6861
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7065 - loss: 0.6872
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65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7056 - loss: 0.6900
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7052 - loss: 0.6907
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84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7054 - loss: 0.6908
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7056 - loss: 0.6908
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7055 - loss: 0.6912
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7055 - loss: 0.6915
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7055 - loss: 0.6917
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7056 - loss: 0.6918
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124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7057 - loss: 0.6920
129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7057 - loss: 0.6923
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7057 - loss: 0.6927
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7056 - loss: 0.6931
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7055 - loss: 0.6935
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7055 - loss: 0.6941
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7054 - loss: 0.6947
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7054 - loss: 0.6954
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7054 - loss: 0.6960
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7054 - loss: 0.6966
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184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7053 - loss: 0.6983
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195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7052 - loss: 0.6993
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204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7052 - loss: 0.7002
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7051 - loss: 0.7008
214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7050 - loss: 0.7015
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7049 - loss: 0.7021
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7048 - loss: 0.7027
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7047 - loss: 0.7032
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7046 - loss: 0.7037
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7046 - loss: 0.7042
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7045 - loss: 0.7046
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7045 - loss: 0.7050
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7044 - loss: 0.7054
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7043 - loss: 0.7058
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7043 - loss: 0.7062
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7043 - loss: 0.7065
274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7042 - loss: 0.7069
279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7042 - loss: 0.7072
284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7042 - loss: 0.7075
289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7042 - loss: 0.7078
294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7041 - loss: 0.7080
299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7041 - loss: 0.7082
304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7041 - loss: 0.7085
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7041 - loss: 0.7087
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7041 - loss: 0.7089
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7040 - loss: 0.7092
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7040 - loss: 0.7094
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7040 - loss: 0.7096
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7039 - loss: 0.7098
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7039 - loss: 0.7100
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7039 - loss: 0.7102
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7039 - loss: 0.7104
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7039 - loss: 0.7106
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7039 - loss: 0.7107
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7039 - loss: 0.7109
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7039 - loss: 0.7110
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7039 - loss: 0.7112
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7039 - loss: 0.7113
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7039 - loss: 0.7115
387/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7039 - loss: 0.7116
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7039 - loss: 0.7117
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7040 - loss: 0.7117
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7040 - loss: 0.7119
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7040 - loss: 0.7119
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7040 - loss: 0.7120
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7040 - loss: 0.7121
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7040 - loss: 0.7122
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7041 - loss: 0.7123
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7041 - loss: 0.7124
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7041 - loss: 0.7125
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7041 - loss: 0.7126
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7041 - loss: 0.7127
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7041 - loss: 0.7128
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7041 - loss: 0.7129
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7041 - loss: 0.7129
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7042 - loss: 0.7130
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7042 - loss: 0.7131
Epoch 17: val_accuracy improved from 0.73157 to 0.73639, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7042 - loss: 0.7132 - val_accuracy: 0.7364 - val_loss: 0.6671 - learning_rate: 0.0100
Epoch 18/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 52s 111ms/step - accuracy: 0.7188 - loss: 0.5769
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7065 - loss: 0.6426
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7143 - loss: 0.6584
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7160 - loss: 0.6664
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7142 - loss: 0.6733
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7117 - loss: 0.6818
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7099 - loss: 0.6892
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7092 - loss: 0.6939
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7090 - loss: 0.6972
45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7091 - loss: 0.6991
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7091 - loss: 0.6999
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7092 - loss: 0.7002
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7089 - loss: 0.7009
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7090 - loss: 0.7013
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7094 - loss: 0.7014
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7098 - loss: 0.7012
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7099 - loss: 0.7010
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7098 - loss: 0.7010
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7098 - loss: 0.7009
92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7097 - loss: 0.7009
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7097 - loss: 0.7010
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7097 - loss: 0.7010
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7097 - loss: 0.7012
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7095 - loss: 0.7015
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7094 - loss: 0.7019
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7093 - loss: 0.7023
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7092 - loss: 0.7025
128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7092 - loss: 0.7027
132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7091 - loss: 0.7030
137/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7091 - loss: 0.7031
142/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7092 - loss: 0.7032
147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7093 - loss: 0.7032
152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7093 - loss: 0.7033
157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7034
162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7034
167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7035
172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7036
177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7037
182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7037
187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7039
192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7040
197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7093 - loss: 0.7042
202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7093 - loss: 0.7043
207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7092 - loss: 0.7045
212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7092 - loss: 0.7046
217/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7092 - loss: 0.7046
222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7047
227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7048
232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7049
237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7049
242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7050
247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7050
252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7090 - loss: 0.7051
257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7090 - loss: 0.7051
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7090 - loss: 0.7052
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7090 - loss: 0.7052
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7052
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7053
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7053
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7053
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7092 - loss: 0.7052
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7092 - loss: 0.7052
302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7093 - loss: 0.7051
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7093 - loss: 0.7051
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7094 - loss: 0.7050
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7095 - loss: 0.7049
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7095 - loss: 0.7049
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7096 - loss: 0.7048
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7096 - loss: 0.7048
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7096 - loss: 0.7047
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7097 - loss: 0.7047
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7097 - loss: 0.7047
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7097 - loss: 0.7046
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7098 - loss: 0.7046
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7098 - loss: 0.7045
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7098 - loss: 0.7045
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7099 - loss: 0.7045
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7099 - loss: 0.7045
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7099 - loss: 0.7044
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7100 - loss: 0.7044
390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7100 - loss: 0.7044
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7100 - loss: 0.7044
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7100 - loss: 0.7043
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7043
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7043
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7043
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7042
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7042
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7042
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7042
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7042
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7042
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7102 - loss: 0.7041
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7102 - loss: 0.7041
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7102 - loss: 0.7041
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7102 - loss: 0.7041
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7102 - loss: 0.7040
Epoch 18: val_accuracy did not improve from 0.73639
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7102 - loss: 0.7040 - val_accuracy: 0.7151 - val_loss: 0.6985 - learning_rate: 0.0100
Epoch 19/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.7188 - loss: 0.6711
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7308 - loss: 0.6339
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7283 - loss: 0.6507
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7304 - loss: 0.6562
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7296 - loss: 0.6586
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7287 - loss: 0.6592
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7270 - loss: 0.6615
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7253 - loss: 0.6647
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7242 - loss: 0.6671
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7234 - loss: 0.6690
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7233 - loss: 0.6700
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7230 - loss: 0.6710
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7222 - loss: 0.6724
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7216 - loss: 0.6737
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7211 - loss: 0.6743
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7207 - loss: 0.6749
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7202 - loss: 0.6758
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7197 - loss: 0.6769
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7193 - loss: 0.6780
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7188 - loss: 0.6792
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7184 - loss: 0.6803
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7181 - loss: 0.6809
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7180 - loss: 0.6813
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7180 - loss: 0.6819
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7178 - loss: 0.6826
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7176 - loss: 0.6833
130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7174 - loss: 0.6839
135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7172 - loss: 0.6846
140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7171 - loss: 0.6851
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7169 - loss: 0.6856
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7169 - loss: 0.6859
155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7168 - loss: 0.6863
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7167 - loss: 0.6866
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7166 - loss: 0.6869
170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7165 - loss: 0.6872
175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7164 - loss: 0.6876
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7163 - loss: 0.6880
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7162 - loss: 0.6882
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7161 - loss: 0.6885
195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7160 - loss: 0.6888
200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7159 - loss: 0.6890
205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7159 - loss: 0.6892
210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7158 - loss: 0.6894
215/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7158 - loss: 0.6895
220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7157 - loss: 0.6896
225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7156 - loss: 0.6898
230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7156 - loss: 0.6899
235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7156 - loss: 0.6900
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7155 - loss: 0.6900
245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7155 - loss: 0.6901
250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7154 - loss: 0.6902
255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7154 - loss: 0.6903
260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7153 - loss: 0.6904
265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7153 - loss: 0.6905
270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7152 - loss: 0.6906
275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7151 - loss: 0.6907
280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7150 - loss: 0.6908
285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7150 - loss: 0.6909
290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7149 - loss: 0.6910
295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7149 - loss: 0.6911
300/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7148 - loss: 0.6912
305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7147 - loss: 0.6913
310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7146 - loss: 0.6914
315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7145 - loss: 0.6916
320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7145 - loss: 0.6917
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7144 - loss: 0.6918
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7144 - loss: 0.6919
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7143 - loss: 0.6919
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7143 - loss: 0.6920
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7142 - loss: 0.6921
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7141 - loss: 0.6923
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7141 - loss: 0.6924
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7140 - loss: 0.6925
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7140 - loss: 0.6927
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7139 - loss: 0.6928
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7139 - loss: 0.6929
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7138 - loss: 0.6930
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7138 - loss: 0.6931
390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7137 - loss: 0.6932
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7137 - loss: 0.6933
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7137 - loss: 0.6934
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7137 - loss: 0.6935
410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7136 - loss: 0.6936
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7136 - loss: 0.6937
421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7135 - loss: 0.6938
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7135 - loss: 0.6939
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7135 - loss: 0.6940
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7134 - loss: 0.6941
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7134 - loss: 0.6942
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7134 - loss: 0.6943
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7134 - loss: 0.6944
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7133 - loss: 0.6945
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7133 - loss: 0.6946
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7133 - loss: 0.6946
Epoch 19: val_accuracy did not improve from 0.73639
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7132 - loss: 0.6948 - val_accuracy: 0.6484 - val_loss: 0.8758 - learning_rate: 0.0100
Epoch 20/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.6875 - loss: 0.7259
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6937 - loss: 0.7433
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6996 - loss: 0.7294
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7042 - loss: 0.7285
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7061 - loss: 0.7263
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7077 - loss: 0.7225
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7077 - loss: 0.7215
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7074 - loss: 0.7191
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7071 - loss: 0.7178
46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7066 - loss: 0.7166
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7067 - loss: 0.7157
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7064 - loss: 0.7159
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7064 - loss: 0.7152
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7063 - loss: 0.7146
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7065 - loss: 0.7141
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7067 - loss: 0.7136
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7069 - loss: 0.7131
86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7071 - loss: 0.7128
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7070 - loss: 0.7131
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7071 - loss: 0.7134
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7071 - loss: 0.7135
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7071 - loss: 0.7136
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7072 - loss: 0.7134
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7074 - loss: 0.7131
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7076 - loss: 0.7127
126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7078 - loss: 0.7125
131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7079 - loss: 0.7125
136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7080 - loss: 0.7124
141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7080 - loss: 0.7123
146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7081 - loss: 0.7123
151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7081 - loss: 0.7122
157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7082 - loss: 0.7122
162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7081 - loss: 0.7122
167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7080 - loss: 0.7123
172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7079 - loss: 0.7124
176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7079 - loss: 0.7124
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7079 - loss: 0.7123
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7080 - loss: 0.7122
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7080 - loss: 0.7121
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7081 - loss: 0.7120
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7082 - loss: 0.7119
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7083 - loss: 0.7117
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7084 - loss: 0.7116
214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7085 - loss: 0.7115
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7086 - loss: 0.7114
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7087 - loss: 0.7112
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7088 - loss: 0.7111
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7089 - loss: 0.7110
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7090 - loss: 0.7109
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7107
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7092 - loss: 0.7106
252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7093 - loss: 0.7105
257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7094 - loss: 0.7103
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7095 - loss: 0.7101
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7097 - loss: 0.7100
271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7098 - loss: 0.7098
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7099 - loss: 0.7097
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7100 - loss: 0.7095
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7101 - loss: 0.7093
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7102 - loss: 0.7091
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7104 - loss: 0.7089
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7105 - loss: 0.7087
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7106 - loss: 0.7085
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7107 - loss: 0.7083
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7108 - loss: 0.7081
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7109 - loss: 0.7078
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7111 - loss: 0.7076
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7112 - loss: 0.7074
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7113 - loss: 0.7072
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7114 - loss: 0.7069
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.7066
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7116 - loss: 0.7064
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7117 - loss: 0.7062
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7118 - loss: 0.7061
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7118 - loss: 0.7059
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7119 - loss: 0.7057
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7120 - loss: 0.7056
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7120 - loss: 0.7054
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7121 - loss: 0.7053
389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7122 - loss: 0.7051
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7122 - loss: 0.7050
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7123 - loss: 0.7048
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7123 - loss: 0.7047
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7124 - loss: 0.7045
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7125 - loss: 0.7043
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7125 - loss: 0.7042
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7126 - loss: 0.7040
429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7127 - loss: 0.7039
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7127 - loss: 0.7037
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7128 - loss: 0.7036
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7128 - loss: 0.7035
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7129 - loss: 0.7033
454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7130 - loss: 0.7032
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7130 - loss: 0.7031
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7131 - loss: 0.7030
473/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7131 - loss: 0.7028
Epoch 20: val_accuracy did not improve from 0.73639
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7132 - loss: 0.7028 - val_accuracy: 0.7038 - val_loss: 0.7000 - learning_rate: 0.0100
Epoch 21/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.5938 - loss: 0.8505
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6697 - loss: 0.7586
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6994 - loss: 0.7075
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7144 - loss: 0.6771
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7220 - loss: 0.6639
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7256 - loss: 0.6573
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7264 - loss: 0.6570
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7273 - loss: 0.6570
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7270 - loss: 0.6597
45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7270 - loss: 0.6621
49/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7271 - loss: 0.6633
53/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7271 - loss: 0.6641
58/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7268 - loss: 0.6656
63/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7264 - loss: 0.6673
67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7262 - loss: 0.6683
72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7259 - loss: 0.6696
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7257 - loss: 0.6702
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7253 - loss: 0.6712
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7251 - loss: 0.6721
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7249 - loss: 0.6728
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7249 - loss: 0.6734
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6740
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6746
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6749
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6752
118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6754
123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6757
128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6760
133/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7247 - loss: 0.6763
138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7247 - loss: 0.6766
143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7246 - loss: 0.6768
148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7245 - loss: 0.6772
153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7244 - loss: 0.6776
158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7243 - loss: 0.6779
163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7243 - loss: 0.6783
168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7242 - loss: 0.6786
173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7242 - loss: 0.6789
178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7241 - loss: 0.6792
183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7241 - loss: 0.6795
188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7240 - loss: 0.6798
193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7239 - loss: 0.6801
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7238 - loss: 0.6804
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7236 - loss: 0.6807
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7235 - loss: 0.6809
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7234 - loss: 0.6812
218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7233 - loss: 0.6814
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7232 - loss: 0.6817
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7231 - loss: 0.6819
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7230 - loss: 0.6821
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7229 - loss: 0.6823
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7229 - loss: 0.6825
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7228 - loss: 0.6827
252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7227 - loss: 0.6828
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7227 - loss: 0.6829
260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7226 - loss: 0.6830
265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7226 - loss: 0.6832
270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7225 - loss: 0.6834
275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7224 - loss: 0.6836
280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7223 - loss: 0.6837
285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7223 - loss: 0.6838
290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7222 - loss: 0.6839
295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7222 - loss: 0.6840
300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7222 - loss: 0.6841
304/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7221 - loss: 0.6841
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7221 - loss: 0.6842
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7221 - loss: 0.6843
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7220 - loss: 0.6844
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7220 - loss: 0.6844
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7219 - loss: 0.6845
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7219 - loss: 0.6845
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7219 - loss: 0.6846
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6846
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6847
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6847
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6847
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6847
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6847
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6848
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6848
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7217 - loss: 0.6848
389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7217 - loss: 0.6848
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7217 - loss: 0.6849
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7217 - loss: 0.6849
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7216 - loss: 0.6850
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7216 - loss: 0.6850
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7216 - loss: 0.6851
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7215 - loss: 0.6851
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7215 - loss: 0.6852
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7215 - loss: 0.6853
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7215 - loss: 0.6853
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7214 - loss: 0.6854
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7214 - loss: 0.6854
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7214 - loss: 0.6855
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7214 - loss: 0.6855
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7214 - loss: 0.6856
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7214 - loss: 0.6856
Epoch 21: val_accuracy did not improve from 0.73639
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7213 - loss: 0.6857 - val_accuracy: 0.7259 - val_loss: 0.6908 - learning_rate: 0.0100
Epoch 22/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.5938 - loss: 0.8932
4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 17ms/step - accuracy: 0.6862 - loss: 0.7238
7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6974 - loss: 0.7087
12/473 ━━━━━━━━━━━━━━━━━━━━ 7s 16ms/step - accuracy: 0.7118 - loss: 0.6925
17/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.7218 - loss: 0.6747
22/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7267 - loss: 0.6617
27/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7302 - loss: 0.6510
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7318 - loss: 0.6457
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7317 - loss: 0.6429
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7318 - loss: 0.6409
45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7315 - loss: 0.6416
50/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7315 - loss: 0.6417
55/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7318 - loss: 0.6417
60/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7319 - loss: 0.6426
64/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7321 - loss: 0.6435
69/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7325 - loss: 0.6443
73/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7326 - loss: 0.6453
78/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7326 - loss: 0.6465
81/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7325 - loss: 0.6472
86/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7326 - loss: 0.6484
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7328 - loss: 0.6493
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7328 - loss: 0.6503
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7328 - loss: 0.6513
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7328 - loss: 0.6522
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7327 - loss: 0.6531
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7326 - loss: 0.6540
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7324 - loss: 0.6549
126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7323 - loss: 0.6555
131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7323 - loss: 0.6559
136/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7322 - loss: 0.6563
141/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7322 - loss: 0.6568
146/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7322 - loss: 0.6572
151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7322 - loss: 0.6575
156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7321 - loss: 0.6579
161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7321 - loss: 0.6582
166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7321 - loss: 0.6585
171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7321 - loss: 0.6588
176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7320 - loss: 0.6591
181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7320 - loss: 0.6595
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7319 - loss: 0.6599
191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7319 - loss: 0.6602
196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7319 - loss: 0.6605
201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7319 - loss: 0.6608
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7319 - loss: 0.6611
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7318 - loss: 0.6614
216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7318 - loss: 0.6617
221/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7318 - loss: 0.6620
226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7317 - loss: 0.6624
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7316 - loss: 0.6628
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7315 - loss: 0.6632
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7315 - loss: 0.6636
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7314 - loss: 0.6640
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7314 - loss: 0.6643
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7314 - loss: 0.6646
261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7313 - loss: 0.6649
266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7313 - loss: 0.6652
271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7312 - loss: 0.6655
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7312 - loss: 0.6657
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7311 - loss: 0.6660
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7311 - loss: 0.6662
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7310 - loss: 0.6664
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7310 - loss: 0.6666
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7310 - loss: 0.6668
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7310 - loss: 0.6670
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7309 - loss: 0.6671
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7309 - loss: 0.6672
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7309 - loss: 0.6673
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7309 - loss: 0.6674
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7309 - loss: 0.6674
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7308 - loss: 0.6674
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7308 - loss: 0.6675
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7308 - loss: 0.6675
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7308 - loss: 0.6676
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7308 - loss: 0.6676
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7308 - loss: 0.6677
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6677
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6677
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6678
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6678
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6678
390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7307 - loss: 0.6678
395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7307 - loss: 0.6679
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7306 - loss: 0.6679
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7306 - loss: 0.6680
410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7306 - loss: 0.6680
415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7306 - loss: 0.6681
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7306 - loss: 0.6681
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7306 - loss: 0.6682
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7305 - loss: 0.6682
434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7305 - loss: 0.6683
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7305 - loss: 0.6684
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7304 - loss: 0.6684
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7304 - loss: 0.6685
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7304 - loss: 0.6686
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6687
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6688
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6689
Epoch 22: val_accuracy did not improve from 0.73639
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7302 - loss: 0.6690 - val_accuracy: 0.7330 - val_loss: 0.6548 - learning_rate: 0.0100
Epoch 23/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.6562 - loss: 0.8340
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6575 - loss: 0.7864
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6615 - loss: 0.7766
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6748 - loss: 0.7523
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6822 - loss: 0.7359
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6898 - loss: 0.7229
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6958 - loss: 0.7128
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6995 - loss: 0.7062
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7023 - loss: 0.7011
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7053 - loss: 0.6955
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7078 - loss: 0.6910
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7098 - loss: 0.6873
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7112 - loss: 0.6846
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7122 - loss: 0.6824
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7131 - loss: 0.6806
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7139 - loss: 0.6791
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7147 - loss: 0.6780
86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7153 - loss: 0.6775
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7161 - loss: 0.6769
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7165 - loss: 0.6766
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7168 - loss: 0.6765
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7171 - loss: 0.6764
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7173 - loss: 0.6765
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7174 - loss: 0.6766
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7177 - loss: 0.6767
126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7180 - loss: 0.6766
131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7183 - loss: 0.6765
136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7187 - loss: 0.6764
141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7190 - loss: 0.6762
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7192 - loss: 0.6762
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7194 - loss: 0.6761
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7196 - loss: 0.6761
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7198 - loss: 0.6761
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7200 - loss: 0.6760
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7202 - loss: 0.6760
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7204 - loss: 0.6759
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7205 - loss: 0.6759
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7207 - loss: 0.6758
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7208 - loss: 0.6758
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7209 - loss: 0.6758
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7210 - loss: 0.6758
202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7211 - loss: 0.6758
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7212 - loss: 0.6759
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7214 - loss: 0.6759
216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7215 - loss: 0.6759
221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7216 - loss: 0.6759
226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7218 - loss: 0.6759
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7218 - loss: 0.6759
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7219 - loss: 0.6760
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7220 - loss: 0.6760
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7221 - loss: 0.6760
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7221 - loss: 0.6760
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7222 - loss: 0.6760
261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7222 - loss: 0.6760
266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7223 - loss: 0.6760
271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7224 - loss: 0.6759
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7225 - loss: 0.6759
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7226 - loss: 0.6759
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7226 - loss: 0.6759
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7227 - loss: 0.6760
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7228 - loss: 0.6760
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7228 - loss: 0.6760
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7229 - loss: 0.6761
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7230 - loss: 0.6761
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7230 - loss: 0.6761
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7231 - loss: 0.6762
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7232 - loss: 0.6762
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7232 - loss: 0.6762
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7233 - loss: 0.6763
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7233 - loss: 0.6764
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7233 - loss: 0.6764
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7234 - loss: 0.6765
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7234 - loss: 0.6765
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7234 - loss: 0.6766
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6766
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6766
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6767
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6767
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6767
389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6767
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6767
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6767
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7238 - loss: 0.6767
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7238 - loss: 0.6767
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7238 - loss: 0.6767
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7239 - loss: 0.6767
422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7239 - loss: 0.6767
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7239 - loss: 0.6767
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7240 - loss: 0.6767
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7240 - loss: 0.6767
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7240 - loss: 0.6767
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7241 - loss: 0.6767
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7241 - loss: 0.6767
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7241 - loss: 0.6767
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7241 - loss: 0.6767
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7242 - loss: 0.6767
Epoch 23: val_accuracy did not improve from 0.73639
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7242 - loss: 0.6768 - val_accuracy: 0.6779 - val_loss: 0.8059 - learning_rate: 0.0100
Epoch 24/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.8125 - loss: 0.4045
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7590 - loss: 0.5475
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7540 - loss: 0.5671
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7467 - loss: 0.5879
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7454 - loss: 0.5960
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7439 - loss: 0.6032
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7412 - loss: 0.6117
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7384 - loss: 0.6201
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7356 - loss: 0.6293
45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7345 - loss: 0.6349
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7345 - loss: 0.6379
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7346 - loss: 0.6399
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7347 - loss: 0.6414
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7349 - loss: 0.6422
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7351 - loss: 0.6427
73/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7353 - loss: 0.6431
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7355 - loss: 0.6433
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7358 - loss: 0.6437
86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7360 - loss: 0.6441
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7362 - loss: 0.6444
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7364 - loss: 0.6449
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7366 - loss: 0.6454
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7366 - loss: 0.6461
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7366 - loss: 0.6468
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7366 - loss: 0.6474
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7368 - loss: 0.6478
126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7368 - loss: 0.6482
131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7369 - loss: 0.6488
136/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7368 - loss: 0.6493
141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7368 - loss: 0.6498
146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7368 - loss: 0.6502
151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7368 - loss: 0.6506
156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7367 - loss: 0.6509
160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7367 - loss: 0.6512
165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7366 - loss: 0.6516
170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7366 - loss: 0.6519
175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7365 - loss: 0.6521
180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7365 - loss: 0.6524
185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7364 - loss: 0.6526
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7363 - loss: 0.6529
195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7362 - loss: 0.6531
200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7361 - loss: 0.6534
205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7360 - loss: 0.6536
210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7359 - loss: 0.6539
214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7358 - loss: 0.6541
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7356 - loss: 0.6543
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7355 - loss: 0.6545
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7355 - loss: 0.6547
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7354 - loss: 0.6549
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7353 - loss: 0.6551
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7352 - loss: 0.6554
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7351 - loss: 0.6556
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7349 - loss: 0.6558
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7348 - loss: 0.6561
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7347 - loss: 0.6563
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7346 - loss: 0.6566
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7345 - loss: 0.6568
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7344 - loss: 0.6571
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7343 - loss: 0.6573
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7342 - loss: 0.6575
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7341 - loss: 0.6577
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7340 - loss: 0.6580
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7339 - loss: 0.6582
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7338 - loss: 0.6585
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7337 - loss: 0.6587
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7336 - loss: 0.6590
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7335 - loss: 0.6592
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7334 - loss: 0.6595
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7333 - loss: 0.6596
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7332 - loss: 0.6597
340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7332 - loss: 0.6599
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7331 - loss: 0.6600
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7330 - loss: 0.6601
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7330 - loss: 0.6602
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7329 - loss: 0.6603
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7329 - loss: 0.6604
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7328 - loss: 0.6606
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7328 - loss: 0.6607
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7327 - loss: 0.6608
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7327 - loss: 0.6609
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7326 - loss: 0.6610
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7325 - loss: 0.6611
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7325 - loss: 0.6612
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7324 - loss: 0.6613
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7323 - loss: 0.6615
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7323 - loss: 0.6616
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7322 - loss: 0.6617
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7322 - loss: 0.6618
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7321 - loss: 0.6620
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7321 - loss: 0.6621
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7320 - loss: 0.6622
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7320 - loss: 0.6623
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7319 - loss: 0.6624
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7319 - loss: 0.6625
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7318 - loss: 0.6626
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7318 - loss: 0.6627
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7317 - loss: 0.6629
Epoch 24: val_accuracy did not improve from 0.73639
473/473 ━━━━━━━━━━━━━━━━━━━━ 7s 14ms/step - accuracy: 0.7317 - loss: 0.6629 - val_accuracy: 0.7358 - val_loss: 0.6609 - learning_rate: 0.0100
Epoch 25/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.7500 - loss: 0.6800
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7105 - loss: 0.6740
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7132 - loss: 0.6721
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7241 - loss: 0.6575
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7298 - loss: 0.6495
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7319 - loss: 0.6477
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7317 - loss: 0.6498
36/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7318 - loss: 0.6510
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7319 - loss: 0.6516
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7321 - loss: 0.6514
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7322 - loss: 0.6520
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7323 - loss: 0.6524
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7325 - loss: 0.6525
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7327 - loss: 0.6528
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7325 - loss: 0.6536
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7324 - loss: 0.6545
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7322 - loss: 0.6555
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7322 - loss: 0.6560
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7320 - loss: 0.6568
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7318 - loss: 0.6576
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7317 - loss: 0.6581
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7314 - loss: 0.6589
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7313 - loss: 0.6594
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7311 - loss: 0.6598
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7310 - loss: 0.6600
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7309 - loss: 0.6604
130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7307 - loss: 0.6610
135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7306 - loss: 0.6615
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7305 - loss: 0.6618
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7304 - loss: 0.6623
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7303 - loss: 0.6627
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7302 - loss: 0.6630
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7301 - loss: 0.6634
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7301 - loss: 0.6636
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7300 - loss: 0.6639
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7299 - loss: 0.6642
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7298 - loss: 0.6644
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7297 - loss: 0.6647
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7296 - loss: 0.6648
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7296 - loss: 0.6650
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7295 - loss: 0.6652
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7294 - loss: 0.6653
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7293 - loss: 0.6655
214/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7292 - loss: 0.6656
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7292 - loss: 0.6657
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7292 - loss: 0.6658
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7291 - loss: 0.6659
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7291 - loss: 0.6660
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7291 - loss: 0.6661
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7291 - loss: 0.6662
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7290 - loss: 0.6663
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7290 - loss: 0.6664
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7290 - loss: 0.6665
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7290 - loss: 0.6665
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7290 - loss: 0.6666
274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7290 - loss: 0.6667
279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7289 - loss: 0.6667
284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7289 - loss: 0.6668
289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7289 - loss: 0.6669
294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7288 - loss: 0.6670
299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7288 - loss: 0.6671
304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7287 - loss: 0.6673
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7287 - loss: 0.6674
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7287 - loss: 0.6675
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7286 - loss: 0.6676
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7286 - loss: 0.6677
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7286 - loss: 0.6677
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7285 - loss: 0.6678
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7285 - loss: 0.6679
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7285 - loss: 0.6680
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7284 - loss: 0.6681
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7284 - loss: 0.6682
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7283 - loss: 0.6683
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7283 - loss: 0.6684
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7282 - loss: 0.6685
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7282 - loss: 0.6686
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7282 - loss: 0.6688
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7281 - loss: 0.6689
389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6690
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6690
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6691
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6692
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6693
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6693
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6694
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6695
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6695
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6696
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6696
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6697
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6697
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6698
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6698
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6698
464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6699
472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6700
Epoch 25: val_accuracy did not improve from 0.73639
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7280 - loss: 0.6700 - val_accuracy: 0.7008 - val_loss: 0.7168 - learning_rate: 0.0100
Epoch 26/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.6875 - loss: 0.5993
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7466 - loss: 0.5960
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7279 - loss: 0.6320
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7238 - loss: 0.6403
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7242 - loss: 0.6406
27/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7209 - loss: 0.6485
32/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7202 - loss: 0.6524
37/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7201 - loss: 0.6556
42/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7198 - loss: 0.6579
47/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7197 - loss: 0.6596
52/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7197 - loss: 0.6608
57/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7199 - loss: 0.6614
62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7204 - loss: 0.6611
67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7208 - loss: 0.6606
72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7213 - loss: 0.6599
77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7217 - loss: 0.6591
82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7221 - loss: 0.6583
87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7224 - loss: 0.6577
92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7228 - loss: 0.6570
97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7232 - loss: 0.6564
102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7236 - loss: 0.6559
107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7238 - loss: 0.6556
112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7241 - loss: 0.6551
117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7243 - loss: 0.6549
122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7244 - loss: 0.6548
127/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7245 - loss: 0.6546
132/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7246 - loss: 0.6546
137/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7248 - loss: 0.6545
142/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7248 - loss: 0.6544
147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7249 - loss: 0.6544
152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7250 - loss: 0.6544
157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7250 - loss: 0.6544
162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7251 - loss: 0.6544
167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7252 - loss: 0.6545
171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7252 - loss: 0.6546
176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7253 - loss: 0.6547
181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7253 - loss: 0.6549
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7254 - loss: 0.6550
191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7254 - loss: 0.6552
196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7255 - loss: 0.6553
201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7256 - loss: 0.6554
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7257 - loss: 0.6555
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7258 - loss: 0.6556
216/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7259 - loss: 0.6557
221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7259 - loss: 0.6559
226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7260 - loss: 0.6560
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7260 - loss: 0.6562
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7261 - loss: 0.6563
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7262 - loss: 0.6564
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7262 - loss: 0.6566
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7263 - loss: 0.6567
255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7264 - loss: 0.6568
260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7264 - loss: 0.6569
265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7265 - loss: 0.6569
270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7266 - loss: 0.6570
275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7267 - loss: 0.6571
280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7267 - loss: 0.6572
285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7268 - loss: 0.6573
290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7269 - loss: 0.6573
295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7270 - loss: 0.6574
299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7270 - loss: 0.6575
304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7271 - loss: 0.6575
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7271 - loss: 0.6576
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7272 - loss: 0.6577
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7272 - loss: 0.6578
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7273 - loss: 0.6579
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7273 - loss: 0.6580
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7274 - loss: 0.6580
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7274 - loss: 0.6581
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7275 - loss: 0.6582
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7275 - loss: 0.6583
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7275 - loss: 0.6583
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7276 - loss: 0.6584
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7276 - loss: 0.6585
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7276 - loss: 0.6585
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7277 - loss: 0.6586
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7277 - loss: 0.6586
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7277 - loss: 0.6587
388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7278 - loss: 0.6587
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7278 - loss: 0.6587
397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7278 - loss: 0.6588
402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7279 - loss: 0.6588
407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7279 - loss: 0.6589
412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6589
417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6590
422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6590
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6590
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6591
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7282 - loss: 0.6591
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7282 - loss: 0.6592
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7282 - loss: 0.6592
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7282 - loss: 0.6593
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7283 - loss: 0.6593
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7283 - loss: 0.6594
467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7283 - loss: 0.6595
Epoch 26: val_accuracy did not improve from 0.73639
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7284 - loss: 0.6596 - val_accuracy: 0.6723 - val_loss: 0.7985 - learning_rate: 0.0100
Epoch 27/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 54s 116ms/step - accuracy: 0.7188 - loss: 0.6842
5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7296 - loss: 0.6304
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7376 - loss: 0.6214
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7359 - loss: 0.6316
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7376 - loss: 0.6336
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7386 - loss: 0.6333
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7385 - loss: 0.6349
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7372 - loss: 0.6379
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7363 - loss: 0.6404
45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7353 - loss: 0.6437
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7341 - loss: 0.6470
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7328 - loss: 0.6501
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7317 - loss: 0.6521
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7307 - loss: 0.6538
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7300 - loss: 0.6550
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7294 - loss: 0.6562
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7290 - loss: 0.6570
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7287 - loss: 0.6575
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7284 - loss: 0.6578
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7283 - loss: 0.6579
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7282 - loss: 0.6578
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7280 - loss: 0.6577
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7279 - loss: 0.6576
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7279 - loss: 0.6575
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7277 - loss: 0.6576
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7276 - loss: 0.6578
129/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6580
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7273 - loss: 0.6580
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7273 - loss: 0.6579
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7273 - loss: 0.6580
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7273 - loss: 0.6580
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6579
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6578
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6578
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6578
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6577
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6576
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6576
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6575
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6574
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6574
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7275 - loss: 0.6573
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7275 - loss: 0.6573
214/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7276 - loss: 0.6572
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7277 - loss: 0.6571
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7277 - loss: 0.6570
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7278 - loss: 0.6570
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7279 - loss: 0.6569
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7280 - loss: 0.6568
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7281 - loss: 0.6567
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7282 - loss: 0.6566
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7283 - loss: 0.6564
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7284 - loss: 0.6563
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7285 - loss: 0.6561
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7286 - loss: 0.6561
274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7287 - loss: 0.6560
279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7288 - loss: 0.6559
284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7289 - loss: 0.6559
289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7290 - loss: 0.6558
294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7291 - loss: 0.6557
299/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7292 - loss: 0.6557
304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7292 - loss: 0.6557
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7293 - loss: 0.6557
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7293 - loss: 0.6557
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7294 - loss: 0.6557
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7294 - loss: 0.6558
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7294 - loss: 0.6558
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7294 - loss: 0.6559
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7294 - loss: 0.6559
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7294 - loss: 0.6559
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7295 - loss: 0.6560
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7295 - loss: 0.6560
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7295 - loss: 0.6560
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7295 - loss: 0.6560
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7296 - loss: 0.6559
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7296 - loss: 0.6559
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7297 - loss: 0.6559
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7297 - loss: 0.6558
388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7298 - loss: 0.6558
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7298 - loss: 0.6557
397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7299 - loss: 0.6557
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7299 - loss: 0.6557
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7300 - loss: 0.6557
411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7300 - loss: 0.6556
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7301 - loss: 0.6556
421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7301 - loss: 0.6557
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7301 - loss: 0.6557
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7302 - loss: 0.6557
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7302 - loss: 0.6557
439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7302 - loss: 0.6557
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7302 - loss: 0.6558
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6558
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6558
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6559
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6559
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6559
Epoch 27: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.
Epoch 27: val_accuracy did not improve from 0.73639
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7304 - loss: 0.6560 - val_accuracy: 0.7275 - val_loss: 0.6787 - learning_rate: 0.0100
Epoch 28/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 51s 109ms/step - accuracy: 0.7812 - loss: 0.5975
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7595 - loss: 0.6545
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7536 - loss: 0.6613
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7491 - loss: 0.6581
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7494 - loss: 0.6543
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7482 - loss: 0.6543
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7492 - loss: 0.6515
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7510 - loss: 0.6473
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7524 - loss: 0.6444
45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7532 - loss: 0.6430
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7542 - loss: 0.6408
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7550 - loss: 0.6392
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7558 - loss: 0.6374
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7565 - loss: 0.6360
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7569 - loss: 0.6354
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7573 - loss: 0.6350
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7577 - loss: 0.6346
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7579 - loss: 0.6341
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7581 - loss: 0.6335
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7583 - loss: 0.6328
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7585 - loss: 0.6321
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7588 - loss: 0.6315
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7589 - loss: 0.6311
112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7590 - loss: 0.6309
117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7591 - loss: 0.6304
122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7592 - loss: 0.6300
127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7592 - loss: 0.6298
132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7592 - loss: 0.6296
137/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7592 - loss: 0.6294
142/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7592 - loss: 0.6292
147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7591 - loss: 0.6288
152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7591 - loss: 0.6285
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Epoch 28: val_accuracy improved from 0.73639 to 0.77175, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 7s 14ms/step - accuracy: 0.7568 - loss: 0.6196 - val_accuracy: 0.7718 - val_loss: 0.5836 - learning_rate: 0.0020
Epoch 29/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.9375 - loss: 0.3694
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272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7577 - loss: 0.6013
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7579 - loss: 0.6011
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7580 - loss: 0.6009
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7581 - loss: 0.6007
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7582 - loss: 0.6006
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7583 - loss: 0.6004
302/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7584 - loss: 0.6003
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7585 - loss: 0.6001
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7586 - loss: 0.5999
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7587 - loss: 0.5997
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7588 - loss: 0.5996
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7589 - loss: 0.5994
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7589 - loss: 0.5993
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7590 - loss: 0.5992
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7591 - loss: 0.5990
343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7592 - loss: 0.5989
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7593 - loss: 0.5988
353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7593 - loss: 0.5986
358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7594 - loss: 0.5985
363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7595 - loss: 0.5983
368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7596 - loss: 0.5982
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7596 - loss: 0.5981
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7597 - loss: 0.5980
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7597 - loss: 0.5979
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7598 - loss: 0.5978
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7599 - loss: 0.5977
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7599 - loss: 0.5976
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7600 - loss: 0.5975
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7600 - loss: 0.5974
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7601 - loss: 0.5973
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7602 - loss: 0.5972
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7602 - loss: 0.5971
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7603 - loss: 0.5970
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7603 - loss: 0.5969
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7604 - loss: 0.5968
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7604 - loss: 0.5967
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7605 - loss: 0.5967
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7605 - loss: 0.5966
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7606 - loss: 0.5965
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7606 - loss: 0.5965
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7606 - loss: 0.5964
Epoch 29: val_accuracy did not improve from 0.77175
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7607 - loss: 0.5963 - val_accuracy: 0.7621 - val_loss: 0.6008 - learning_rate: 0.0020
Epoch 30/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 1:02 133ms/step - accuracy: 0.6562 - loss: 0.6860
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7345 - loss: 0.5972
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7429 - loss: 0.5894
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7498 - loss: 0.5843
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7523 - loss: 0.5831
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7521 - loss: 0.5843
32/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7525 - loss: 0.5871
37/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7521 - loss: 0.5918
42/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7520 - loss: 0.5947
47/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7525 - loss: 0.5960
52/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7534 - loss: 0.5962
57/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7545 - loss: 0.5960
62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7554 - loss: 0.5956
67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7558 - loss: 0.5958
72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7559 - loss: 0.5960
77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7560 - loss: 0.5962
82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7560 - loss: 0.5966
87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7559 - loss: 0.5972
92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7559 - loss: 0.5975
97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7558 - loss: 0.5976
102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7558 - loss: 0.5976
107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7558 - loss: 0.5976
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7558 - loss: 0.5976
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7558 - loss: 0.5977
120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7557 - loss: 0.5978
125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7558 - loss: 0.5978
129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7558 - loss: 0.5977
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7559 - loss: 0.5975
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7559 - loss: 0.5975
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7558 - loss: 0.5975
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7557 - loss: 0.5976
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7556 - loss: 0.5978
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7555 - loss: 0.5978
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7554 - loss: 0.5978
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7554 - loss: 0.5979
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7553 - loss: 0.5980
177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7552 - loss: 0.5981
181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7551 - loss: 0.5983
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7551 - loss: 0.5984
191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7550 - loss: 0.5986
196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7550 - loss: 0.5986
201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7549 - loss: 0.5987
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7549 - loss: 0.5987
210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7549 - loss: 0.5988
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7549 - loss: 0.5988
218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7549 - loss: 0.5988
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7549 - loss: 0.5987
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7549 - loss: 0.5987
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7549 - loss: 0.5987
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7549 - loss: 0.5987
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7550 - loss: 0.5987
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7550 - loss: 0.5987
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7550 - loss: 0.5986
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7551 - loss: 0.5986
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7551 - loss: 0.5985
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7552 - loss: 0.5984
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7553 - loss: 0.5983
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7554 - loss: 0.5981
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7554 - loss: 0.5980
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7555 - loss: 0.5979
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7556 - loss: 0.5977
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7557 - loss: 0.5976
302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7558 - loss: 0.5974
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7559 - loss: 0.5973
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7559 - loss: 0.5972
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7560 - loss: 0.5971
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7561 - loss: 0.5969
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7561 - loss: 0.5968
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7562 - loss: 0.5967
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7562 - loss: 0.5966
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7562 - loss: 0.5965
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7563 - loss: 0.5965
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7563 - loss: 0.5964
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7564 - loss: 0.5963
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7564 - loss: 0.5962
363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7565 - loss: 0.5962
367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7565 - loss: 0.5961
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7566 - loss: 0.5960
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7566 - loss: 0.5959
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7566 - loss: 0.5959
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7567 - loss: 0.5958
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7567 - loss: 0.5957
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7567 - loss: 0.5956
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Epoch 30: val_accuracy improved from 0.77175 to 0.77597, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 7s 14ms/step - accuracy: 0.7573 - loss: 0.5949 - val_accuracy: 0.7760 - val_loss: 0.5733 - learning_rate: 0.0020
Epoch 31/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.7188 - loss: 0.6817
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7110 - loss: 0.6854
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7198 - loss: 0.6743
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7256 - loss: 0.6530
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7322 - loss: 0.6365
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7365 - loss: 0.6262
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224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7570 - loss: 0.5940
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7570 - loss: 0.5939
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7571 - loss: 0.5938
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7572 - loss: 0.5936
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7573 - loss: 0.5934
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264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7576 - loss: 0.5926
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274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7578 - loss: 0.5923
279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7579 - loss: 0.5921
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7579 - loss: 0.5919
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7580 - loss: 0.5918
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7581 - loss: 0.5916
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307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7583 - loss: 0.5912
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7583 - loss: 0.5911
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7584 - loss: 0.5910
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7585 - loss: 0.5909
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7585 - loss: 0.5908
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7586 - loss: 0.5908
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374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7589 - loss: 0.5906
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439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7593 - loss: 0.5901
444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7594 - loss: 0.5901
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7594 - loss: 0.5901
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7594 - loss: 0.5900
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7595 - loss: 0.5900
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7595 - loss: 0.5900
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7596 - loss: 0.5899
Epoch 31: val_accuracy did not improve from 0.77597
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7596 - loss: 0.5899 - val_accuracy: 0.7748 - val_loss: 0.5714 - learning_rate: 0.0020
Epoch 32/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.9375 - loss: 0.4048
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.8208 - loss: 0.5240
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7941 - loss: 0.5454
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7859 - loss: 0.5497
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7804 - loss: 0.5549
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7771 - loss: 0.5598
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7759 - loss: 0.5614
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7749 - loss: 0.5626
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7737 - loss: 0.5641
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7731 - loss: 0.5651
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7730 - loss: 0.5660
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7731 - loss: 0.5663
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7730 - loss: 0.5667
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7732 - loss: 0.5669
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7734 - loss: 0.5670
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7734 - loss: 0.5674
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7734 - loss: 0.5678
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7732 - loss: 0.5684
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7728 - loss: 0.5692
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7726 - loss: 0.5696
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7726 - loss: 0.5698
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7726 - loss: 0.5698
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7726 - loss: 0.5698
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7725 - loss: 0.5699
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7725 - loss: 0.5699
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7725 - loss: 0.5701
129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7725 - loss: 0.5702
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7724 - loss: 0.5704
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7724 - loss: 0.5706
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7723 - loss: 0.5709
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7722 - loss: 0.5712
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7721 - loss: 0.5716
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7720 - loss: 0.5719
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7720 - loss: 0.5721
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7720 - loss: 0.5724
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7719 - loss: 0.5727
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7719 - loss: 0.5729
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7719 - loss: 0.5730
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7719 - loss: 0.5732
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7719 - loss: 0.5733
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7719 - loss: 0.5735
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7718 - loss: 0.5737
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7718 - loss: 0.5738
214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7718 - loss: 0.5739
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7718 - loss: 0.5740
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7718 - loss: 0.5741
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5743
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5744
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5745
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5745
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5745
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5745
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5745
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5744
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5745
274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5745
279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5745
284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5744
289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5744
294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5744
299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5744
303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7715 - loss: 0.5744
308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7715 - loss: 0.5745
313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7715 - loss: 0.5745
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7715 - loss: 0.5746
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7715 - loss: 0.5746
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7715 - loss: 0.5747
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7714 - loss: 0.5747
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7714 - loss: 0.5748
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7714 - loss: 0.5749
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7713 - loss: 0.5750
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7713 - loss: 0.5751
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7712 - loss: 0.5752
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7712 - loss: 0.5753
367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7711 - loss: 0.5753
372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7711 - loss: 0.5754
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7711 - loss: 0.5755
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7710 - loss: 0.5755
387/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7710 - loss: 0.5756
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7709 - loss: 0.5757
397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7709 - loss: 0.5757
402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7709 - loss: 0.5757
407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7708 - loss: 0.5758
412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7708 - loss: 0.5759
417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7707 - loss: 0.5759
422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7707 - loss: 0.5760
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7707 - loss: 0.5760
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7706 - loss: 0.5761
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7706 - loss: 0.5761
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7705 - loss: 0.5762
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7705 - loss: 0.5763
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7705 - loss: 0.5763
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7704 - loss: 0.5763
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7704 - loss: 0.5764
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7704 - loss: 0.5765
Epoch 32: val_accuracy did not improve from 0.77597
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7703 - loss: 0.5765 - val_accuracy: 0.7728 - val_loss: 0.5806 - learning_rate: 0.0020
Epoch 33/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 54s 116ms/step - accuracy: 0.7812 - loss: 0.5685
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.8049 - loss: 0.5255
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.8066 - loss: 0.5192
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7994 - loss: 0.5301
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7946 - loss: 0.5398
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7907 - loss: 0.5470
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7889 - loss: 0.5497
36/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7866 - loss: 0.5524
41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7846 - loss: 0.5546
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7832 - loss: 0.5556
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7826 - loss: 0.5558
54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7822 - loss: 0.5561
57/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7818 - loss: 0.5567
62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7811 - loss: 0.5577
67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7803 - loss: 0.5591
72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7796 - loss: 0.5602
77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7791 - loss: 0.5607
82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7788 - loss: 0.5610
87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7786 - loss: 0.5610
92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7784 - loss: 0.5611
97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7781 - loss: 0.5613
102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7777 - loss: 0.5616
107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7773 - loss: 0.5619
112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7768 - loss: 0.5623
117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7764 - loss: 0.5627
122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7761 - loss: 0.5630
127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7757 - loss: 0.5633
132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7754 - loss: 0.5637
137/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7750 - loss: 0.5642
142/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7747 - loss: 0.5645
147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7745 - loss: 0.5649
152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7743 - loss: 0.5651
157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7741 - loss: 0.5653
162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7739 - loss: 0.5655
167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7737 - loss: 0.5657
172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7735 - loss: 0.5658
178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7733 - loss: 0.5659
182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7731 - loss: 0.5660
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7729 - loss: 0.5662
190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7728 - loss: 0.5663
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7727 - loss: 0.5663
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7725 - loss: 0.5664
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7723 - loss: 0.5666
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7722 - loss: 0.5667
214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7721 - loss: 0.5668
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7719 - loss: 0.5670
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7718 - loss: 0.5671
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5673
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5674
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7715 - loss: 0.5675
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7714 - loss: 0.5676
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7713 - loss: 0.5677
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7712 - loss: 0.5679
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7711 - loss: 0.5681
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7711 - loss: 0.5682
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7710 - loss: 0.5683
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7710 - loss: 0.5685
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7709 - loss: 0.5686
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7708 - loss: 0.5687
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7708 - loss: 0.5688
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7707 - loss: 0.5689
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7707 - loss: 0.5690
302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7706 - loss: 0.5691
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7706 - loss: 0.5693
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7705 - loss: 0.5695
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7704 - loss: 0.5697
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7704 - loss: 0.5698
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7703 - loss: 0.5700
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7702 - loss: 0.5701
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7702 - loss: 0.5703
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7701 - loss: 0.5704
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7701 - loss: 0.5705
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7700 - loss: 0.5707
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7700 - loss: 0.5708
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7700 - loss: 0.5709
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7699 - loss: 0.5710
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7699 - loss: 0.5711
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7698 - loss: 0.5712
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7698 - loss: 0.5713
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7698 - loss: 0.5713
390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7698 - loss: 0.5714
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5714
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5715
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5716
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5716
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7696 - loss: 0.5717
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7696 - loss: 0.5717
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7696 - loss: 0.5718
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7695 - loss: 0.5719
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7695 - loss: 0.5719
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7695 - loss: 0.5720
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7695 - loss: 0.5721
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7694 - loss: 0.5721
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7694 - loss: 0.5722
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7694 - loss: 0.5722
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7694 - loss: 0.5723
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7694 - loss: 0.5723
Epoch 33: val_accuracy did not improve from 0.77597
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7694 - loss: 0.5724 - val_accuracy: 0.7683 - val_loss: 0.5852 - learning_rate: 0.0020
Epoch 34/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.9062 - loss: 0.5441
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.8330 - loss: 0.5340
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.8097 - loss: 0.5601
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7990 - loss: 0.5729
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7934 - loss: 0.5746
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7898 - loss: 0.5757
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7871 - loss: 0.5765
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7857 - loss: 0.5761
42/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7845 - loss: 0.5762
46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7839 - loss: 0.5760
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7837 - loss: 0.5749
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7835 - loss: 0.5738
59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7832 - loss: 0.5731
64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7829 - loss: 0.5727
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7829 - loss: 0.5720
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7830 - loss: 0.5715
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7831 - loss: 0.5712
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7831 - loss: 0.5707
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7830 - loss: 0.5703
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7828 - loss: 0.5702
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7825 - loss: 0.5702
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7823 - loss: 0.5702
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7821 - loss: 0.5701
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7818 - loss: 0.5699
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7816 - loss: 0.5697
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7814 - loss: 0.5696
129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7811 - loss: 0.5696
133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7809 - loss: 0.5696
137/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7806 - loss: 0.5696
141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7803 - loss: 0.5697
145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7801 - loss: 0.5697
150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7798 - loss: 0.5696
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7796 - loss: 0.5696
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7794 - loss: 0.5696
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7792 - loss: 0.5696
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7790 - loss: 0.5696
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7788 - loss: 0.5696
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7786 - loss: 0.5697
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7783 - loss: 0.5697
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7781 - loss: 0.5698
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7780 - loss: 0.5698
198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7778 - loss: 0.5698
203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7777 - loss: 0.5699
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7775 - loss: 0.5699
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7774 - loss: 0.5700
218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7772 - loss: 0.5700
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7771 - loss: 0.5701
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7769 - loss: 0.5703
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7768 - loss: 0.5704
237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7766 - loss: 0.5705
242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7765 - loss: 0.5706
247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7763 - loss: 0.5707
252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7762 - loss: 0.5708
257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7761 - loss: 0.5709
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7759 - loss: 0.5710
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7758 - loss: 0.5711
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7757 - loss: 0.5711
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7757 - loss: 0.5711
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7756 - loss: 0.5712
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7756 - loss: 0.5712
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7755 - loss: 0.5712
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7754 - loss: 0.5713
302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7754 - loss: 0.5713
307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7754 - loss: 0.5713
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7753 - loss: 0.5714
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7753 - loss: 0.5714
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7752 - loss: 0.5714
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7752 - loss: 0.5715
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7751 - loss: 0.5715
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7751 - loss: 0.5715
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7751 - loss: 0.5715
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7750 - loss: 0.5715
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7750 - loss: 0.5715
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7750 - loss: 0.5715
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7749 - loss: 0.5716
367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7749 - loss: 0.5716
372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7748 - loss: 0.5716
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7748 - loss: 0.5716
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7748 - loss: 0.5716
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7748 - loss: 0.5716
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7748 - loss: 0.5716
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7748 - loss: 0.5715
397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7748 - loss: 0.5715
402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7747 - loss: 0.5715
407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7747 - loss: 0.5715
412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7747 - loss: 0.5715
417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7747 - loss: 0.5715
422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7747 - loss: 0.5715
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7746 - loss: 0.5716
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7746 - loss: 0.5716
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7746 - loss: 0.5716
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7745 - loss: 0.5717
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7745 - loss: 0.5717
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7745 - loss: 0.5718
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7744 - loss: 0.5718
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7744 - loss: 0.5718
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7744 - loss: 0.5719
Epoch 34: val_accuracy did not improve from 0.77597
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7743 - loss: 0.5719 - val_accuracy: 0.7517 - val_loss: 0.6262 - learning_rate: 0.0020
Epoch 35/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.7188 - loss: 0.5507
5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7626 - loss: 0.5096
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7751 - loss: 0.5021
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7801 - loss: 0.5036
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7823 - loss: 0.5072
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7826 - loss: 0.5126
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7815 - loss: 0.5194
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7804 - loss: 0.5248
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7796 - loss: 0.5290
45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7790 - loss: 0.5322
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7785 - loss: 0.5346
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7779 - loss: 0.5372
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7773 - loss: 0.5396
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7769 - loss: 0.5417
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7765 - loss: 0.5439
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7761 - loss: 0.5458
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7755 - loss: 0.5481
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7747 - loss: 0.5505
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7740 - loss: 0.5526
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7734 - loss: 0.5544
100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7730 - loss: 0.5559
105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7729 - loss: 0.5570
110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7729 - loss: 0.5576
115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7729 - loss: 0.5583
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7729 - loss: 0.5588
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7730 - loss: 0.5591
129/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7730 - loss: 0.5594
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7730 - loss: 0.5598
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7730 - loss: 0.5602
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7730 - loss: 0.5604
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7730 - loss: 0.5606
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7730 - loss: 0.5608
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7731 - loss: 0.5610
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7731 - loss: 0.5611
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7731 - loss: 0.5612
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7732 - loss: 0.5614
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7732 - loss: 0.5614
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7733 - loss: 0.5614
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7734 - loss: 0.5614
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7735 - loss: 0.5613
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7737 - loss: 0.5612
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7738 - loss: 0.5611
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7739 - loss: 0.5610
213/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7740 - loss: 0.5610
218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7740 - loss: 0.5610
223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7741 - loss: 0.5610
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7741 - loss: 0.5610
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7742 - loss: 0.5610
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7743 - loss: 0.5609
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7743 - loss: 0.5609
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7743 - loss: 0.5608
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7743 - loss: 0.5608
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7744 - loss: 0.5607
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7744 - loss: 0.5606
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7744 - loss: 0.5606
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7745 - loss: 0.5605
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7745 - loss: 0.5605
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7745 - loss: 0.5605
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7744 - loss: 0.5604
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7744 - loss: 0.5604
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7744 - loss: 0.5604
301/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7744 - loss: 0.5604
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7744 - loss: 0.5605
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7744 - loss: 0.5605
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7744 - loss: 0.5604
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7744 - loss: 0.5604
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7743 - loss: 0.5604
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7743 - loss: 0.5604
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7743 - loss: 0.5604
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7742 - loss: 0.5604
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7742 - loss: 0.5604
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7741 - loss: 0.5605
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7741 - loss: 0.5606
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7741 - loss: 0.5607
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7740 - loss: 0.5608
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7740 - loss: 0.5608
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7739 - loss: 0.5609
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7739 - loss: 0.5610
386/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7739 - loss: 0.5611
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7739 - loss: 0.5611
394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7739 - loss: 0.5611
399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7739 - loss: 0.5612
404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7739 - loss: 0.5612
409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7738 - loss: 0.5613
414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7738 - loss: 0.5613
419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7738 - loss: 0.5614
424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7738 - loss: 0.5614
428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7738 - loss: 0.5615
433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7737 - loss: 0.5616
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7737 - loss: 0.5617
443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7737 - loss: 0.5617
448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7737 - loss: 0.5618
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7736 - loss: 0.5619
458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7736 - loss: 0.5619
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7736 - loss: 0.5620
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7736 - loss: 0.5621
Epoch 35: val_accuracy did not improve from 0.77597
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7735 - loss: 0.5621 - val_accuracy: 0.7744 - val_loss: 0.5716 - learning_rate: 0.0020
Epoch 36/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 106ms/step - accuracy: 0.7812 - loss: 0.5845
5/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.8071 - loss: 0.5976
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7929 - loss: 0.5918
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7875 - loss: 0.5906
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7822 - loss: 0.5917
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7781 - loss: 0.5922
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7742 - loss: 0.5930
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7721 - loss: 0.5924
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7707 - loss: 0.5913
44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7699 - loss: 0.5902
48/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7696 - loss: 0.5892
53/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7689 - loss: 0.5886
58/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7683 - loss: 0.5883
63/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7678 - loss: 0.5877
68/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7674 - loss: 0.5868
73/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7672 - loss: 0.5860
78/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7672 - loss: 0.5852
83/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7671 - loss: 0.5847
88/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7671 - loss: 0.5842
93/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7672 - loss: 0.5838
98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7674 - loss: 0.5833
103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7675 - loss: 0.5830
108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7677 - loss: 0.5826
113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7679 - loss: 0.5821
118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7681 - loss: 0.5817
123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7682 - loss: 0.5813
128/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7684 - loss: 0.5810
133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7685 - loss: 0.5806
138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7686 - loss: 0.5803
143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7686 - loss: 0.5801
148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7687 - loss: 0.5799
153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7687 - loss: 0.5798
158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7688 - loss: 0.5795
163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7688 - loss: 0.5792
168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7689 - loss: 0.5790
172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7689 - loss: 0.5789
177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7689 - loss: 0.5787
182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7689 - loss: 0.5786
187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7690 - loss: 0.5784
192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7690 - loss: 0.5783
197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7691 - loss: 0.5782
202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7691 - loss: 0.5782
207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7691 - loss: 0.5781
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7690 - loss: 0.5781
215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7690 - loss: 0.5780
220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5780
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5779
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5779
233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5778
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5777
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5777
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5775
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7691 - loss: 0.5774
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7691 - loss: 0.5773
262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7691 - loss: 0.5772
267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5770
272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5769
277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5768
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5767
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5767
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5766
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5766
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5765
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7693 - loss: 0.5765
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7693 - loss: 0.5764
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7693 - loss: 0.5763
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7693 - loss: 0.5762
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7693 - loss: 0.5761
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7693 - loss: 0.5761
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7694 - loss: 0.5760
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7694 - loss: 0.5759
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7694 - loss: 0.5758
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7694 - loss: 0.5757
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7694 - loss: 0.5756
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7694 - loss: 0.5755
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7695 - loss: 0.5754
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7695 - loss: 0.5753
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7696 - loss: 0.5751
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7696 - loss: 0.5750
386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7696 - loss: 0.5749
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7696 - loss: 0.5748
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5747
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5746
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5745
411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5744
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7698 - loss: 0.5744
421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7698 - loss: 0.5743
426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7698 - loss: 0.5742
431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7698 - loss: 0.5741
436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7699 - loss: 0.5741
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7699 - loss: 0.5740
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7699 - loss: 0.5740
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7699 - loss: 0.5739
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7699 - loss: 0.5739
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7700 - loss: 0.5738
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7700 - loss: 0.5737
Epoch 36: ReduceLROnPlateau reducing learning rate to 0.0003999999724328518.
Epoch 36: val_accuracy did not improve from 0.77597
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7700 - loss: 0.5736 - val_accuracy: 0.7691 - val_loss: 0.5893 - learning_rate: 0.0020
Epoch 37/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.6250 - loss: 0.9760
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7396 - loss: 0.7031
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7486 - loss: 0.6541
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7510 - loss: 0.6334
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7540 - loss: 0.6194
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7574 - loss: 0.6089
32/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7595 - loss: 0.6003
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Epoch 37: val_accuracy improved from 0.77597 to 0.77718, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7754 - loss: 0.5516 - val_accuracy: 0.7772 - val_loss: 0.5747 - learning_rate: 4.0000e-04
Epoch 38/60
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Epoch 38: val_accuracy improved from 0.77718 to 0.77959, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 7s 14ms/step - accuracy: 0.7851 - loss: 0.5388 - val_accuracy: 0.7796 - val_loss: 0.5663 - learning_rate: 4.0000e-04
Epoch 39/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.8125 - loss: 0.5589
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261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7889 - loss: 0.5320
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271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7886 - loss: 0.5324
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7885 - loss: 0.5325
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7883 - loss: 0.5327
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7881 - loss: 0.5329
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7880 - loss: 0.5331
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7878 - loss: 0.5333
301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7876 - loss: 0.5335
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7875 - loss: 0.5338
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7873 - loss: 0.5340
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7872 - loss: 0.5341
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7870 - loss: 0.5344
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7869 - loss: 0.5346
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7867 - loss: 0.5348
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7866 - loss: 0.5350
342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7865 - loss: 0.5351
347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7864 - loss: 0.5353
352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7863 - loss: 0.5354
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7862 - loss: 0.5356
362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7861 - loss: 0.5357
367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7860 - loss: 0.5358
372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7859 - loss: 0.5360
377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7858 - loss: 0.5361
382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7857 - loss: 0.5362
387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7856 - loss: 0.5363
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7855 - loss: 0.5364
397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7855 - loss: 0.5365
402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7854 - loss: 0.5366
407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7853 - loss: 0.5367
412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7852 - loss: 0.5368
417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7852 - loss: 0.5369
422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7851 - loss: 0.5370
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7850 - loss: 0.5370
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7850 - loss: 0.5371
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7849 - loss: 0.5372
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5372
447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5373
452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7847 - loss: 0.5374
457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7846 - loss: 0.5375
462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7846 - loss: 0.5376
468/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7845 - loss: 0.5376
Epoch 39: val_accuracy did not improve from 0.77959
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7844 - loss: 0.5377 - val_accuracy: 0.7780 - val_loss: 0.5734 - learning_rate: 4.0000e-04
Epoch 40/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.6875 - loss: 0.9082
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7243 - loss: 0.7213
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7514 - loss: 0.6566
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7626 - loss: 0.6235
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7689 - loss: 0.6036
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7708 - loss: 0.5943
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7726 - loss: 0.5864
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7742 - loss: 0.5808
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7759 - loss: 0.5753
46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7776 - loss: 0.5703
51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7790 - loss: 0.5663
56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7802 - loss: 0.5627
61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7809 - loss: 0.5605
66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7816 - loss: 0.5584
71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7821 - loss: 0.5568
76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7824 - loss: 0.5553
81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7828 - loss: 0.5538
86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7830 - loss: 0.5528
91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7830 - loss: 0.5522
96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7829 - loss: 0.5519
101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7829 - loss: 0.5515
106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7828 - loss: 0.5513
111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7827 - loss: 0.5511
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7827 - loss: 0.5507
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7827 - loss: 0.5502
126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7827 - loss: 0.5497
131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7827 - loss: 0.5492
136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7828 - loss: 0.5486
141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7828 - loss: 0.5482
146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7828 - loss: 0.5479
151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7829 - loss: 0.5475
156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7829 - loss: 0.5472
161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7829 - loss: 0.5468
166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7830 - loss: 0.5464
171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7830 - loss: 0.5460
176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7831 - loss: 0.5458
181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7831 - loss: 0.5456
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7831 - loss: 0.5453
191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7831 - loss: 0.5451
196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7832 - loss: 0.5449
201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7832 - loss: 0.5447
206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7832 - loss: 0.5446
211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7832 - loss: 0.5445
216/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7832 - loss: 0.5443
221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7831 - loss: 0.5443
226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7831 - loss: 0.5442
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7831 - loss: 0.5442
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7830 - loss: 0.5441
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7830 - loss: 0.5441
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7830 - loss: 0.5441
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7829 - loss: 0.5441
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7829 - loss: 0.5441
261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7829 - loss: 0.5441
266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7828 - loss: 0.5441
271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7828 - loss: 0.5441
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7827 - loss: 0.5442
281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7826 - loss: 0.5443
286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7826 - loss: 0.5443
291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7826 - loss: 0.5443
296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7825 - loss: 0.5444
301/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7825 - loss: 0.5444
306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7825 - loss: 0.5445
311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445
316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445
321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445
326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445
331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445
336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445
346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5445
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5446
356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5446
361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5446
366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5445
371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5445
376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5445
381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5444
386/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5444
391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5444
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5443
401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5443
406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5442
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Epoch 40: val_accuracy improved from 0.77959 to 0.78139, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7823 - loss: 0.5440 - val_accuracy: 0.7814 - val_loss: 0.5636 - learning_rate: 4.0000e-04
Epoch 41/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 53s 112ms/step - accuracy: 0.8125 - loss: 0.4921
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7864 - loss: 0.5214
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7889 - loss: 0.5285
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7880 - loss: 0.5363
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7857 - loss: 0.5400
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7816 - loss: 0.5456
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54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7743 - loss: 0.5614
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224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7777 - loss: 0.5529
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7778 - loss: 0.5528
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7778 - loss: 0.5527
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7778 - loss: 0.5526
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7779 - loss: 0.5526
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314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7777 - loss: 0.5527
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7778 - loss: 0.5527
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7778 - loss: 0.5526
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7778 - loss: 0.5526
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7778 - loss: 0.5525
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7778 - loss: 0.5524
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352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7779 - loss: 0.5523
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387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7780 - loss: 0.5517
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7780 - loss: 0.5516
397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7780 - loss: 0.5515
402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7781 - loss: 0.5514
407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7781 - loss: 0.5513
412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7781 - loss: 0.5512
416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7781 - loss: 0.5511
421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7782 - loss: 0.5511
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431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7782 - loss: 0.5509
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7782 - loss: 0.5508
438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7782 - loss: 0.5508
442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7783 - loss: 0.5507
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7783 - loss: 0.5507
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7783 - loss: 0.5506
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7783 - loss: 0.5505
459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7783 - loss: 0.5505
463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7784 - loss: 0.5504
470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7784 - loss: 0.5504
Epoch 41: val_accuracy did not improve from 0.78139
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7784 - loss: 0.5503 - val_accuracy: 0.7768 - val_loss: 0.5727 - learning_rate: 4.0000e-04
Epoch 42/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 104ms/step - accuracy: 0.6562 - loss: 0.6868
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7256 - loss: 0.5969
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7479 - loss: 0.5690
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7603 - loss: 0.5500
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7640 - loss: 0.5414
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7658 - loss: 0.5383
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7670 - loss: 0.5366
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7682 - loss: 0.5352
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7697 - loss: 0.5340
45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7716 - loss: 0.5320
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7731 - loss: 0.5308
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7744 - loss: 0.5296
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7754 - loss: 0.5286
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7766 - loss: 0.5274
70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7776 - loss: 0.5265
75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7785 - loss: 0.5256
80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7792 - loss: 0.5250
85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7796 - loss: 0.5249
90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7801 - loss: 0.5247
95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7805 - loss: 0.5246
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7808 - loss: 0.5247
103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7810 - loss: 0.5246
108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7813 - loss: 0.5245
113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7815 - loss: 0.5245
118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7818 - loss: 0.5245
123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7821 - loss: 0.5244
128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7823 - loss: 0.5244
133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7825 - loss: 0.5244
138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7828 - loss: 0.5244
143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7830 - loss: 0.5245
148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7832 - loss: 0.5245
153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7834 - loss: 0.5246
158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7836 - loss: 0.5246
162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7837 - loss: 0.5246
167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7838 - loss: 0.5246
172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7839 - loss: 0.5247
177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7840 - loss: 0.5247
182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7841 - loss: 0.5247
186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7842 - loss: 0.5247
191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7843 - loss: 0.5247
196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7844 - loss: 0.5247
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7844 - loss: 0.5247
202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7844 - loss: 0.5248
205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7844 - loss: 0.5248
208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7845 - loss: 0.5248
213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7845 - loss: 0.5248
218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7845 - loss: 0.5249
223/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7846 - loss: 0.5250
228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7846 - loss: 0.5250
232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7847 - loss: 0.5250
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7847 - loss: 0.5250
240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7847 - loss: 0.5250
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7848 - loss: 0.5250
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7848 - loss: 0.5249
255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7849 - loss: 0.5249
260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7849 - loss: 0.5249
265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7849 - loss: 0.5249
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5248
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5248
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5248
282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5248
287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5249
292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5249
297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5250
302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5250
307/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5251
312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7851 - loss: 0.5252
317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7851 - loss: 0.5252
322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7851 - loss: 0.5253
327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7851 - loss: 0.5254
332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7851 - loss: 0.5255
337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5256
341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5257
345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5257
350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5258
355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5259
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5260
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5261
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5262
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5263
380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5264
385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5265
389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5265
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5266
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5267
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5268
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5268
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5269
418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5270
423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5271
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5272
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5273
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5274
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5275
446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5276
451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5277
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7847 - loss: 0.5278
461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7847 - loss: 0.5280
466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7847 - loss: 0.5281
Epoch 42: val_accuracy did not improve from 0.78139
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7847 - loss: 0.5283 - val_accuracy: 0.7728 - val_loss: 0.5732 - learning_rate: 4.0000e-04
Epoch 43/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.9062 - loss: 0.3541
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8389 - loss: 0.4537
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8196 - loss: 0.4786
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8077 - loss: 0.4943
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8035 - loss: 0.4990
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8019 - loss: 0.4991
31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8004 - loss: 0.5003
36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7992 - loss: 0.5014
41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7981 - loss: 0.5027
45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7976 - loss: 0.5035
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7969 - loss: 0.5048
55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7960 - loss: 0.5062
60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7953 - loss: 0.5073
65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7946 - loss: 0.5083
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7941 - loss: 0.5092
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7934 - loss: 0.5105
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7927 - loss: 0.5116
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7923 - loss: 0.5126
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7919 - loss: 0.5135
93/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7917 - loss: 0.5140
98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7916 - loss: 0.5146
103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7912 - loss: 0.5154
108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7908 - loss: 0.5161
113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7905 - loss: 0.5169
118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7901 - loss: 0.5176
123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7897 - loss: 0.5184
128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7894 - loss: 0.5191
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7889 - loss: 0.5199
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7886 - loss: 0.5205
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7883 - loss: 0.5212
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7880 - loss: 0.5219
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7877 - loss: 0.5226
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7873 - loss: 0.5234
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7871 - loss: 0.5240
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7869 - loss: 0.5247
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7867 - loss: 0.5252
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7865 - loss: 0.5258
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7863 - loss: 0.5263
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7862 - loss: 0.5268
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7860 - loss: 0.5273
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7859 - loss: 0.5278
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7858 - loss: 0.5281
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7857 - loss: 0.5285
214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7857 - loss: 0.5288
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7856 - loss: 0.5291
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7856 - loss: 0.5294
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7855 - loss: 0.5297
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7855 - loss: 0.5299
239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7854 - loss: 0.5301
244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7854 - loss: 0.5304
249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7853 - loss: 0.5306
254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7853 - loss: 0.5309
259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7852 - loss: 0.5311
264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7852 - loss: 0.5313
269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5315
274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5317
279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5319
284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5321
289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5323
294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5325
299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5326
304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5328
309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5329
314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5331
319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5332
324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5334
329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5335
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5337
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5338
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5339
349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5340
354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5342
359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5343
364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5344
369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5345
374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5346
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5346
384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5347
388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7849 - loss: 0.5348
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7849 - loss: 0.5349
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7849 - loss: 0.5350
400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5350
405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5351
410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5352
415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5353
420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5353
425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5354
430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5355
435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5355
440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5356
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5356
449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5357
453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5357
456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5357
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5358
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5358
Epoch 43: val_accuracy did not improve from 0.78139
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7848 - loss: 0.5359 - val_accuracy: 0.7780 - val_loss: 0.5715 - learning_rate: 4.0000e-04
Epoch 44/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.8438 - loss: 0.4009
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7701 - loss: 0.4954
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7623 - loss: 0.5212
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7585 - loss: 0.5332
19/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7581 - loss: 0.5386
23/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7598 - loss: 0.5392
27/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7611 - loss: 0.5401
32/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7636 - loss: 0.5406
37/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7653 - loss: 0.5420
42/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7673 - loss: 0.5413
47/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7688 - loss: 0.5413
52/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7699 - loss: 0.5420
57/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7707 - loss: 0.5420
62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7718 - loss: 0.5414
67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7727 - loss: 0.5410
72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7733 - loss: 0.5410
77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7736 - loss: 0.5415
82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7738 - loss: 0.5422
87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7739 - loss: 0.5427
92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7741 - loss: 0.5433
97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7744 - loss: 0.5435
102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7747 - loss: 0.5434
107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7751 - loss: 0.5431
112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7755 - loss: 0.5429
116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7757 - loss: 0.5427
121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7759 - loss: 0.5426
126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7762 - loss: 0.5424
130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7764 - loss: 0.5424
134/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7765 - loss: 0.5423
139/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7768 - loss: 0.5422
143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7769 - loss: 0.5421
148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7771 - loss: 0.5421
153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7773 - loss: 0.5421
158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7774 - loss: 0.5420
162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7775 - loss: 0.5420
167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7777 - loss: 0.5420
172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7778 - loss: 0.5420
177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7778 - loss: 0.5420
182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7779 - loss: 0.5419
187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7781 - loss: 0.5418
192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7782 - loss: 0.5417
197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7783 - loss: 0.5417
202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7784 - loss: 0.5417
207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7785 - loss: 0.5416
212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7786 - loss: 0.5416
217/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7787 - loss: 0.5415
222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7788 - loss: 0.5413
227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7790 - loss: 0.5412
231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7790 - loss: 0.5411
236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7792 - loss: 0.5409
241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7793 - loss: 0.5407
246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7794 - loss: 0.5406
251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7794 - loss: 0.5405
256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7795 - loss: 0.5403
261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7796 - loss: 0.5402
266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7797 - loss: 0.5401
271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7798 - loss: 0.5401
276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7798 - loss: 0.5400
280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7799 - loss: 0.5400
285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7800 - loss: 0.5399
290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7800 - loss: 0.5399
295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7801 - loss: 0.5398
300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7802 - loss: 0.5397
305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7803 - loss: 0.5396
310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7803 - loss: 0.5396
315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7804 - loss: 0.5395
320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7805 - loss: 0.5394
325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7806 - loss: 0.5393
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7806 - loss: 0.5393
334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7807 - loss: 0.5392
339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7807 - loss: 0.5391
344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7808 - loss: 0.5391
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7809 - loss: 0.5390
351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7809 - loss: 0.5390
357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7810 - loss: 0.5389
360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7810 - loss: 0.5389
365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7810 - loss: 0.5389
370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7811 - loss: 0.5388
375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7811 - loss: 0.5388
379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7812 - loss: 0.5388
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7812 - loss: 0.5388
388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7812 - loss: 0.5387
393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7813 - loss: 0.5387
398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7813 - loss: 0.5387
403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7813 - loss: 0.5387
408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7814 - loss: 0.5387
413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7814 - loss: 0.5387
417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7814 - loss: 0.5387
422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7814 - loss: 0.5387
427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7814 - loss: 0.5387
432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7815 - loss: 0.5387
437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7815 - loss: 0.5386
441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7815 - loss: 0.5386
445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7815 - loss: 0.5386
450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7815 - loss: 0.5386
455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7816 - loss: 0.5386
460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7816 - loss: 0.5386
465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7816 - loss: 0.5386
Epoch 44: val_accuracy did not improve from 0.78139
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7816 - loss: 0.5386 - val_accuracy: 0.7812 - val_loss: 0.5683 - learning_rate: 4.0000e-04
Epoch 45/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.8125 - loss: 0.4872
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8039 - loss: 0.5049
10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8007 - loss: 0.4998
15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8014 - loss: 0.4949
20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8028 - loss: 0.4934
25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8035 - loss: 0.4938
30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8030 - loss: 0.4960
35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8033 - loss: 0.4969
40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8035 - loss: 0.4983
45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8030 - loss: 0.5000
50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8021 - loss: 0.5027
54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8016 - loss: 0.5045
59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8012 - loss: 0.5063
64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8009 - loss: 0.5075
69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8007 - loss: 0.5085
74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8005 - loss: 0.5095
79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8001 - loss: 0.5107
84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7997 - loss: 0.5117
89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7993 - loss: 0.5125
94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7987 - loss: 0.5134
99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7981 - loss: 0.5147
104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7975 - loss: 0.5160
109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7968 - loss: 0.5171
114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7962 - loss: 0.5180
119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7958 - loss: 0.5187
124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7954 - loss: 0.5191
129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7951 - loss: 0.5195
134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7948 - loss: 0.5197
139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7945 - loss: 0.5200
144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7942 - loss: 0.5202
149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7939 - loss: 0.5206
154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7936 - loss: 0.5210
159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7934 - loss: 0.5212
164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7932 - loss: 0.5214
169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7931 - loss: 0.5216
174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7931 - loss: 0.5216
179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7930 - loss: 0.5217
184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7929 - loss: 0.5217
189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7928 - loss: 0.5218
194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7928 - loss: 0.5219
199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7927 - loss: 0.5221
204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7927 - loss: 0.5223
209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7926 - loss: 0.5225
214/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7925 - loss: 0.5226
219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7925 - loss: 0.5228
224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7925 - loss: 0.5229
229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7924 - loss: 0.5230
234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7924 - loss: 0.5230
238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7924 - loss: 0.5232
243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7923 - loss: 0.5233
248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7922 - loss: 0.5234
253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7922 - loss: 0.5236
258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7921 - loss: 0.5237
263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7921 - loss: 0.5238
268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7920 - loss: 0.5239
273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7920 - loss: 0.5240
278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7919 - loss: 0.5241
283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7919 - loss: 0.5242
288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7919 - loss: 0.5243
293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7918 - loss: 0.5245
298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7917 - loss: 0.5246
303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7917 - loss: 0.5247
308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7916 - loss: 0.5249
313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7915 - loss: 0.5250
318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7915 - loss: 0.5252
323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7914 - loss: 0.5253
328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7913 - loss: 0.5255
333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7913 - loss: 0.5257
338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7912 - loss: 0.5259
343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7911 - loss: 0.5260
348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7911 - loss: 0.5262
353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7910 - loss: 0.5264
358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7909 - loss: 0.5265
363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7909 - loss: 0.5267
368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7909 - loss: 0.5268
373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7908 - loss: 0.5269
378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7908 - loss: 0.5271
383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7907 - loss: 0.5272
388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7907 - loss: 0.5273
392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7907 - loss: 0.5274
396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7907 - loss: 0.5274
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473/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7901 - loss: 0.5289
Epoch 45: ReduceLROnPlateau reducing learning rate to 7.999999215826393e-05.
Epoch 45: val_accuracy did not improve from 0.78139
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7901 - loss: 0.5289 - val_accuracy: 0.7798 - val_loss: 0.5694 - learning_rate: 4.0000e-04
Epoch 46/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.8438 - loss: 0.3964
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8165 - loss: 0.5574
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237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7898 - loss: 0.5330
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315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7892 - loss: 0.5344
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325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7891 - loss: 0.5345
330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7890 - loss: 0.5346
335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7890 - loss: 0.5346
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449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7881 - loss: 0.5352
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464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7880 - loss: 0.5354
473/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7880 - loss: 0.5354
Epoch 46: val_accuracy improved from 0.78139 to 0.78220, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7880 - loss: 0.5354 - val_accuracy: 0.7822 - val_loss: 0.5676 - learning_rate: 8.0000e-05
Epoch 47/60
1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 104ms/step - accuracy: 0.6875 - loss: 0.9415
6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7153 - loss: 0.7430
11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7289 - loss: 0.6856
16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7359 - loss: 0.6576
21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7387 - loss: 0.6425
26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7415 - loss: 0.6293
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473/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7737 - loss: 0.5524
Epoch 47: val_accuracy did not improve from 0.78220
473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7737 - loss: 0.5523 - val_accuracy: 0.7818 - val_loss: 0.5682 - learning_rate: 8.0000e-05
Epoch 47: early stopping
Restoring model weights from the end of the best epoch: 40.
Plotting the Training and Validation Accuracies¶
plt.plot(history_complex.history["accuracy"])
plt.plot(history_complex.history["val_accuracy"])
plt.title("Complex CNN Model accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train", "validation"], loc="upper left")
plt.show()
Evaluating the Model on Test Set¶
# Calculate the number of steps for the entire test set to be processed
test_steps = test_generator.samples // batch_size
# If the number of samples isn't a multiple of the batch size,
# you have one more batch with the remaining samples
if test_generator.samples % batch_size > 0:
test_steps += 1
# Evaluating the model on the test set
evaluation_results = model_complex.evaluate(test_generator, steps=test_steps)
print(f"Loss: {evaluation_results[0]}, Accuracy: {evaluation_results[1]}")
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - accuracy: 0.8750 - loss: 0.3037
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.8281 - loss: 0.4269
Loss: 0.46025848388671875, Accuracy: 0.8125
Plotting Confusion Matrix¶
pred_probabilities = model_complex.predict(test_generator, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)
# Getting the true labels from the generator
y_true = test_generator.classes
# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))
# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("Complex CNN Model Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 1s 453ms/step
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step
precision recall f1-score support
happy 0.93 0.88 0.90 32
neutral 0.69 0.84 0.76 32
sad 0.74 0.62 0.68 32
surprise 0.91 0.91 0.91 32
accuracy 0.81 128
macro avg 0.82 0.81 0.81 128
weighted avg 0.82 0.81 0.81 128
Observations and Insights:
- The complex CNN model contains 1,096,452 total parameters with 1,094,148 being trainable. This represents an increment in complexity compared to the previous custom CNN models.
- With an accuracy of 81.25% on the test set, this model has outperformed the previous two custom CNN models, reflecting its ability to generalize better to new data.
- The confusion matrix presents strong performance in recognizing 'happy' and 'surprise' emotions with f1-scores of 0.90 and 0.91, respectively, similar to the other models, but with better results for 'neutral' and 'sad' emotions with f1-scores of 0.76 and 0.68.
- Overall, this model has shown a balanced performance across different emotions and suggests that increasing the complexity with an additional convolutional layer has provided benefits in learning facial emotion features.
- We have tested this model with a dedicated set of hyperparameters batch of tests (experimenting with different learning rates, batch sizes, optimizers) reaching up to 85% of accuracy. However, when trying to reproduce the accuracy with the best parameters, we still got this 81% (great but still a hard line to improve).
Plotting the Confusion Matrix for the chosen final model¶
pred_probabilities = model_complex.predict(test_generator, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)
# Getting the true labels from the generator
y_true = test_generator.classes
# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))
# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("Best Model Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step
precision recall f1-score support
happy 0.93 0.88 0.90 32
neutral 0.69 0.84 0.76 32
sad 0.74 0.62 0.68 32
surprise 0.91 0.91 0.91 32
accuracy 0.81 128
macro avg 0.82 0.81 0.81 128
weighted avg 0.82 0.81 0.81 128
Observations and Insights:
- The model is highly effective at correctly identifying 'happy' and 'surprise' emotions with 28 and 29 correct predictions respectively, out of 32 instances each, indicating strong precision and recall for these classes.
- It performs well on 'neutral' emotion too, with 27 correct predictions, but has some misclassifications as 'sad'.
- The 'sad' emotion is also well-recognized with 20 correct predictions; however, there are 10 instances where 'sad' was incorrectly predicted as 'neutral'.
- The model shows very few instances of confusion between 'happy' and 'sad' or 'surprise', which suggests distinct feature learning for these emotions.
- Overall, the model demonstrates a balanced capability across different emotions with minor confusions, which might be improved with more training data or further tuning of the model architecture and hyperparameters.
Conclusion: The custom complex CNN model, with an accuracy of 81%, outperformed transfer learning architectures, providing an efficient and accurate solution for facial emotion recognition in grayscale images.¶
Insights¶
Refined insights:¶
- What are the most meaningful insights from the data relevant to the problem?
Models show varying degrees of success, with some like the complex CNN achieving a high level of accuracy (over 80%), suggesting that deeper architectures can capture better the features in facial expressions.
Across models, 'happy' and 'surprise' emotions are generally identified with high accuracy, implying that these emotions have distinct features that are easily captured by the CNN layers.
'Neutral' and 'sad' emotions are more challenging for the models to distinguish, often being confused with one another.
Transfer learning models did not outperform the custom complex CNN, which suggests that, for this specific dataset and problem, custom-designed architectures can be more effective than off-the-shelf pre-trained models.
Comparison of various techniques and their relative performance:¶
- How do different techniques perform? Which one is performing relatively better? Is there scope to improve the performance further?
The custom complex CNN model, with additional convolutional layers and parameters, performed the best, achieving an accuracy over 80%. This suggests that custom-tailored architectures can capture the nuances of this particular task more effectively than pre-trained models.
Among the transfer learning models utilized, such as VGG16, ResNet50V2, and EfficientNetV2B0, none outperformed the custom complex CNN model significantly. These models, although powerful, may not have provided substantial improvements due to the specific nature of the dataset, which consists of small grayscale images where fine-tuned features specific to facial emotion recognition might be more beneficial than the generalized features learned from large, diverse datasets these models were trained on.
There may be scope to improve performance:
- Data Augmentation: Testing more sophisticated data augmentation strategies might help the model generalize better, particularly for underperforming classes.
- Increased Data: More training data, particularly for the underrepresented and challenging emotions, could improve model learning.
- Hyperparameter Tuning: Continue tuning the models hyperparameters could optimize model performance.
- Model creation: Continue creating new custom models to outperform the ones generated.
Proposal for the final solution design:¶
What model do you propose to be adopted? Why is this the best solution to adopt?
The custom complex CNN model is proposed for adoption as the final solution.
The model is specifically designed for the task, with layers and features that detects and recognized the characteristics of facial emotions in grayscale images.
It has demonstrated the highest accuracy among all tested models, including transfer learning models.
It remains computationally efficient compared to larger transfer learning models, making it more practical for deployment in real-world applications where resources may be limited.
As a custom model, it offers greater flexibility for further tuning and modification, which could be beneficial for ongoing optimization and adaptation to new data or use cases.
Finally, to be noted that incorporating a facial emotion detection model into real-world applications, such as improving user experience in software interfaces or helping psychological analysis, we need to be really careful about the ethical considerations surrounding its deployment, such as privacy concerns and potential biases.
Recommendations for Implementation¶
Key Recommendations¶
- Data Augmentation: Implement advanced data augmentation techniques to enhance model robustness, especially for less accurate classifications.
- Expand Dataset: Collect and integrate a larger and more diverse dataset to improve the model's learning and generalization capabilities.
- Hyperparameter Optimization: Invest time in fine-tuning the model's hyperparameters to achieve the best possible performance.
- Model Innovation: Develop new custom models that may provide better results than existing ones, taking into account the specific nuances of the problem.
Actionables for Stakeholders¶
- Quality Assurance: Ensure the dataset's integrity and diversity to mitigate quality issues that could affect model training.
- Monitoring and Iteration: Regularly monitor model performance in real-world applications and iterate on the model as needed.
- Ethical and Privacy Review: Conduct thorough reviews to address any potential ethical and privacy concerns prior to deployment.
Expected Benefit/Cost¶
- User Experience Improvement: Enhanced user interactions in digital platforms, potentially leading to increased user satisfaction and engagement.
- Cost of Data Acquisition: Investment may be required to procure or generate a larger and more diverse dataset.
- Computational Resources: Depending on the model complexity, increased computational power may be necessary, incurring higher costs.
Key Risks and Challenges¶
- Data Sensitivity: Managing sensitive user data responsibly to uphold privacy standards.
- Model Generalization: Ensuring the model performs well across diverse real-world scenarios, not just on the training dataset.
- Bias Mitigation: Continuously working to identify and mitigate biases in model predictions.
Further Analysis and Associated Problems¶
- Ongoing Evaluation: Continued analysis of model performance against new data and in varying contexts.
- Complementary Solutions: Investigation into additional solutions that could work alongside the emotion detection model to improve overall system effectiveness
Appendix¶
Project Code Repository Overview¶
The Facial Emotion Detection project leverages a comprehensive code repository hosted on GitHub, serving as a central hub for all the developmental, testing, and final submission artifacts. The repository is structured to facilitate efficient experimentation with model configurations and hyperparameters, which is critical for the iterative process of machine learning model development.
Final Notebook and Batch Testing¶
The centerpiece of the repository is the Final Submission Notebook, which contains the fully executed code along with outputs demonstrating the performance of the final model. This notebook provides transparency and reproducibility, two essential aspects of any data science workflow.
To expedite the model testing phase, the repository includes scripts that enable batch testing of multiple hyperparameter configurations. This approach is delineated in the repository's README file, which outlines how to replicate the model generation process. The corresponding hyperparameters and their permutations are defined within the batch_config.yaml file. This method significantly reduces manual overhead and ensures a methodical exploration of the model's parameter space.
The repository also showcases the complex CNN model configuration in complex_1.yaml, which was identified as the top performer in the project. It is through these YAML configurations that batch processing was enabled, allowing for a comprehensive and automated model testing regime.
Lastly, the results of these model evaluations can be found consolidated under the results directory, providing a quick reference to the performance metrics of each tested model. This structured and systematic approach to model testing underscores the robustness and diligence applied throughout the project lifecycle.